Non-linear Association between Atherogenic Index of Plasma and Disease Activity in Rheumatoid Arthritis: A Cross-sectional Study

Non-linear Association between Atherogenic Index of Plasma and Disease Activity in Rheumatoid Arthritis: A Cross-sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Non-linear Association between Atherogenic Index of Plasma and Disease Activity in Rheumatoid Arthritis: A Cross-sectional Study Lina Leng, Ying Li, Quanyi Tang, Jinfeng Zhang, Yaorong Han, Xiaoli Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7722454/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: To explore the relationship between plasma atherogenic index (AIP) and disease activity of rheumatoid arthritis (RA). Method: This cross-sectional study included a total of 1088 RA patients. According to the third quartile of AIP, patients were divided into three groups (T1, T2, T3), and a multiple linear regression model was used to analyze the association between AIP and 28 joint disease range of motion scores (DAS28-ESR). In addition, a two-stage linear regression model is used to explore potential nonlinear relationships. Result: After adjusting for confounding factors, multiple linear regression showed that, with T1 group as a reference, the DAS28-ESR scores of T2 group (β=0.159, 95% CI: 0.041~0.277) and T3 group (β=0.192, 95% CI: 0.071~0.314) were significantly higher (trend P-value=0.002). The analysis of AIP as a continuous variable also yielded consistent conclusions (β=0.312, 95% CI: 0.080~0.545). However, the two-segment linear regression revealed a significant nonlinear relationship (log likelihood ratio P＜0.001), with an inflection point of AIP=0.175. Before the inflection point (AIP0.175), AIP showed a negative correlation with DAS28-ESR (β=-1.010, 95% CI: -1.682~-0.339). Conclusion: There is a significant nonlinear correlation between AIP and RA disease activity. When AIP is lower than 0.175, it is positively correlated with disease activity; But when AIP exceeds this critical value, the correlation reverses. This result suggests that lipid metabolism may play a complex role in RA inflammation. Health sciences/Biomarkers Health sciences/Diseases Biological sciences/Immunology Health sciences/Medical research Health sciences/Rheumatology Health sciences/Risk factors plasma atherogenic index Rheumatoid arthritis Disease activity level Blood lipids Nonlinear correlation Figures Figure 1 Figure 2 Introduction Rheumatoid arthritis (RA) is a common systemic autoimmune disease characterized by chronic synovitis and progressive joint destruction, causing significant disability burden and decreased quality of life worldwide 1 . In addition to the limitation of musculoskeletal system, RA is clearly considered as an independent risk factor for accelerating atherosclerosis and premature cardiovascular disease (CVD), which is the main cause of death in this patient group 2,3 . This complex connection is largely coordinated by a sustained systemic inflammatory state, in which circulating pro-inflammatory cytokines, especially tumor necrosis factor alpha (TNF - α) and interleukin-6 (IL-6), not only cause joint damage, but also trigger endothelial dysfunction, promote plaque instability, and disrupt metabolic homeostasis 4,5 . Evaluating the cardiovascular risk of RA requires a multifaceted approach that combines traditional risk factors with disease-specific parameters. Among them, dyslipidemia plays a crucial but contradictory and complex role. The classic lipid mass spectrum of active RA usually shows a "lipid paradox", characterized by low levels of total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C), which is in sharp contrast to the typical atherogenic lipid model common in the general population 6,7 . This phenomenon is attributed to the increased inflammatory cascade reaction leading to lipid consumption, which complicates the interpretation of traditional lipid parameters and may underestimate the real risk of atherosclerosis 8 . Therefore, there is an urgent need for a more robust and anti-inflammatory lipid index in clinical practice to accurately reflect cardiovascular risk under autoimmune background. The plasma atherosclerosis index (AIP) is calculated by the base logarithm of 10 of the molar ratio of triglyceride to high-density lipoprotein cholesterol (log (TG/HDL-C)), which has become an effective and comprehensive biomarker of atherosclerotic dyslipidemia 9 . Unlike individual lipid measurements, AIP is closely associated with the presence of small and dense LDL particles, which are a subtype highly sensitive to oxidation and have good arterial intima permeability. They are also closely related to insulin resistance and systemic inflammation 10,11 . Its prognostic value for cardiovascular events has been verified in metabolic syndrome, diabetes and the general population 12,13 . Given that the TG/HDL-C ratio reflects pro-inflammatory and pro thrombotic states, AIP can serve as a mirror to capture the interaction between lipid metabolism and inflammatory burden in RA. Therefore, this study utilized data from 1088 RA patients to comprehensively elucidate the association between AIP and RA disease activity, provide a more complex and detailed understanding of the lipid inflammatory axis of RA, and provide information for future targeted therapy strategies. Method Research Design and Participants This study is a cross-sectional study that included 1088 consecutive RA patients who visited Xingtai People's Hospital. All patients met the RA classification criteria proposed by the American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) in 2010 14 . Exclusion criteria include: severe liver and kidney dysfunction, malignant tumors, and incomplete data. The screening process for research participants is shown in Figure 1. This study was approved by the Ethics Committee of our hospital (batch number: 2025 [031]), and all patients signed informed consent forms. Data Collection Collect demographic data, clinical characteristics, and laboratory indicators of patients through an electronic medical record system. Including: gender, age, course of disease (MH), body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), smoking history, drinking history, hypertension history, diabetes mellitus (DM) history, coronary heart disease (CHD) history, and use of lipid-lowering drugs. Laboratory indicators include: erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), uric acid (UA), fasting blood glucose (FPG), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C). AIP is calculated using the formula: AIP=log (TG/HDL-C). Disease activity was evaluated using the 28 joint disease activity score DAS28-ESR score. Statistical analysis According to the third quartile of AIP, all patients were divided into three groups: T1, T2, and T3. If the measurement data conforms to a normal distribution, it is expressed as mean ± standard deviation, and analysis of variance is used for inter group comparison; If it does not follow a normal distribution, it is represented by the median (interquartile range), and the Kruskal Wallis H test is used for inter group comparison. Count data is presented in terms of examples (percentage), and comparison between groups is conducted using chi square test. Preliminary screening of variables related to DAS28-ESR using single factor linear regression. Subsequently, a multiple linear regression model was constructed to analyze the association between AIP and DAS28-ESR. We established three models: crude model (unadjusted variables), adjusted model I (adjusted for gender, age, BMI) and adjusted model II (further adjusted for hypertension, DM, CHD, use of lipid-lowering drugs, UA, and FPG). To explore nonlinear relationships, we use a two-stage linear regression model and calculate the inflection point. Compare the goodness of fit between the piecewise linear model and the standard linear model using a log likelihood ratio test. All statistical analyses were conducted using R software (version 4.0.3) and SPSS (version 26.0). P<0.05 indicates that the difference is statistically significant. Results Baseline Characteristics A total of 1088 RA patients were included in this study, and their baseline characteristics were grouped according to AIP quartiles as shown in Table 1. There were significant differences in BMI, DBP, diabetes history, ESR, CRP, UA, FPG, TC, TG, HDL-C, LDL-C and DAS28-ESR among the three groups (P<0.05). As AIP levels increase (from T1 to T3), TG, FPG, TC, LDL-C, UA levels, and DAS28-ESR scores show an upward trend, while HDL-C levels significantly decrease. Single factor analysis affecting DAS28-ESR The results of single factor linear regression analysis (Table 2) showed that age (β=0.012, P<0.001), hypertension (β=0.109, P=0.039), diabetes (β=0.170, P=0.047), ESR (β=0.019, P<0.001), CRP (β=0.009, P<0.001), FPG (β=0.061, P=0.005) were positively correlated with DAS28-ESR. But TC（β=-0.083, P=0.001）、TG（β=-0.121, P=0.001）、HDL-C（β=-0.804, P<0.001） It is negatively correlated with DAS28-ESR. It is worth noting that AIP is significantly positively correlated with DAS28-ESR (β=0.262, P=0.022). Multivariate linear regression analysis of AIP and DAS28-ESR As shown in Table 3, in the unadjusted coarse model, for every unit increase in AIP, the DAS28-ESR score significantly increased by 0.262 units (95% CI: 0.039~0.486). After gradually adjusting for gender, age, BMI (Model I), and other metabolic confounding factors (Model II), this positive correlation remained significant, and the effect value even slightly increased (adjusted Model II: β=0.312, 95% CI: 0.080-0.545). After analyzing AIP as a categorical variable (triplet array), the results showed that compared with the T1 group, the DAS28-ESR scores of patients in the T2 and T3 groups were significantly higher, and there was a significant dose-response trend (trend P-value=0.002). Nonlinear Relationship Analysis Given the complexity of lipid metabolism, we further investigated whether there is a nonlinear correlation between AIP and DAS28-ESR. The two-stage linear regression model (Table 4) revealed a significant nonlinear relationship (log likelihood ratio test P<0.001), with an inflection point of AIP=0.175. Within the range of AIP0.175, the relationship between the two reverses and shows a negative correlation (β=-1.010, 95% CI: -1.682~-0.339). See Figure 2. Discussion We confirmed that among 1088 RA patients, higher AIP was associated with increased disease activity, and this association still existed after strict adjustment for confounding factors. Secondly, and most interestingly, our analysis using a two-stage linear regression model revealed a significant nonlinear relationship characterized by a critical inflection point at AIP=0.175. Below this threshold, AIP is strongly positively correlated with DAS28-ESR; Surprisingly, beyond this point, this relationship reversed and showed a negative correlation. These findings deepen our understanding of the complex interactions between lipid metabolism and disease activity in RA. AIP is widely considered as a reliable marker of dyslipidemia and atherosclerosis. Multiple studies have shown that both baseline and cumulative AIP exposure are associated with cardiovascular disease, particularly coronary artery disease 15,16 . In addition, studies have found that higher levels of AIP are positively correlated with the risk of hypertension and non-alcoholic fatty liver disease 17,18 . A cross-sectional study shows that there is a close relationship between the increased AIP level, the increased risk of insulin resistance and the onset of type 2 diabetes 19 . Recent studies have explored the relationship between AIP and cerebrovascular diseases, indicating that a higher level of AIP is associated with a higher incidence of carotid and intracranial atherosclerotic stenosis 20,21 . In terms of RA disease, more research has focused on the relationship between AIP and the risk of developing cardiovascular disease, indicating that AIP can serve as an independent indicator of long-term CVD risk in RA patients 22 . Further investigation is needed to determine whether AIP levels can serve as a biomarker for disease activity in RA patients. To our knowledge, this study provides the first evidence of a non-linear correlation between baseline AIP levels and disease activity in RA patients. Our initial linear model is consistent with an increasing number of literature indicating that dyslipidemia is associated with the inflammatory pathogenesis of RA 23,24 . The positive correlation between AIP and DAS28-ESR, even after adjusting the confounding factors, also strengthens the concept that atherosclerotic dyslipidemia is not only a complication, but also may have an internal relationship with disease activity. Mechanistically, this relationship can be conceptualized through several pathways. Research has shown that the levels of small and dense low-density lipoprotein (sdLDL) are significantly elevated in RA patients, and the ability of LDL to specifically bind to chondroitin sulfate proteoglycans on the surface of macrophages is enhanced 25,26 . In addition, sdLDL can promote the growth of atherosclerosis by regulating lipid metabolism, inducing inflammation and enhancing endothelial damage 27 . It is worth noting that smaller sdLDL particles are more likely to form oxidized LDL, penetrate target cells, and are not easily cleared 28 . Because of this, the LDL receptor is unable to recognize sdLDL, making it easier to be absorbed by macrophages and become foam cells, thus promoting the development and occurrence of atherosclerosis 29 . Simultaneously oxidized LDL can also activate synovial fibroblasts and innate immune cells through scavenger receptors, making pro-inflammatory cytokine cascades (such as IL-1 β, IL-6, TNF - α) permanent, thereby exacerbating systemic inflammation and joint damage 30 . In addition, insulin resistance (AIP is a recognized alternative biomarker) can lead to a pro-inflammatory state characterized by elevated levels of free fatty acids and adipokines, further exacerbating the progression of RA disease 31 . However, the most provocative finding in our study was that the inversion of the AIP-DAS28 relationship exceeded the threshold of 0.175. This phenomenon can be explained by the well described 'lipid paradox' in RA and other chronic inflammatory states. We hypothesize a biphasic model of lipid inflammation interaction: in the mild to moderate inflammatory stage (usually corresponding to low to moderate AIP levels), positive correlation dominates. Here, metabolic abnormalities drive inflammation through the aforementioned mechanisms. On the contrary, in severe and uncontrolled inflammatory states (which may occur simultaneously with very high AIP levels), the classic lipid paradox becomes apparent. High levels of systemic cytokines can significantly inhibit lipoprotein lipase activity, leading to hypertriglyceridemia, while inhibiting lecithin cholesterol acyltransferase (LCAT) enzyme, reducing the maturity and function of HDL-C 32 . More importantly, rampant inflammatory reactions may lead to the depletion of lipid components, which are used as energy substrates by overactive immune cells or incorporated into cell membranes during rapid immune cell proliferation 33 . This consumption effect may paradoxically lead to a relative decrease in measured disease activity, which explains the negative slope observed at very high AIP levels. Therefore, this inflection point (AIP=0.175) may represent a critical metabolic inflammatory threshold. Our research must be explained within the context of its limitations. The cross-sectional design excludes any inference of causal relationships; We cannot determine whether AIP triggers inflammation, whether inflammation alters lipid metabolism, or whether both processes are influenced by a third unmeasured factor. Although we have adjusted for various confounding factors, residual confounding factors are still a possibility. For example, there is no detailed data available for adjusting dietary habits and physical activity levels. Finally, as a single center study, the generalizability of our findings, including specific inflection point values, needs to be validated in larger multicenter prospective cohorts. Conclusion In summary, our research indicates that the relationship between AIP and RA disease activity is not linear, but rather has a significant threshold effect. Future prospective research is crucial for verifying this turning point and exploring the potential mechanisms driving this biphasic relationship. Ultimately, understanding this complex interaction can provide a basis for more personalized management strategies targeting both inflammation and lipid metabolism in RA patients. Declarations Data availability The data will be shared on reasonable request to the corresponding or the senior author. Acknowledgements The author thanks Xingtai People's Hospital for providing data on this platform and thanks all participants for their selfless dedication. Competing interests The authors declare no competing interests. Author contributions LL and XL proposed the strategy, LL conducted the data extraction and organization as well as the final draft, QT provided statistical methodological guidance, LL, YL, JZ and YH analyzed and validated the data, and QT and YL produced the charts. All authors conducted the review and approved the final manuscript. LL and XL are the guarantors of this work and are responsible for the completeness and accuracy of the work. Funding Statement This study was supported by Key R&D Projects in Xingtai City (No. 2025ZC074). Ethics approval and consent to participate This study received approval from the Research Ethics Committee of Xingtai People’s Hospital (approval number: 2025[031]). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. References McInnes IB, Schett G. The pathogenesis of rheumatoid arthritis. N Engl J Med. 8;365(23):2205-19 (2011). England BR, Thiele GM, Anderson DR, Mikuls TR. Increased cardiovascular risk in rheumatoid arthritis: mechanisms and implications. BMJ. 23;361:k1036 (2018). Hannawi S, Hannawi H, Al Salmi I. Cardiovascular disease and subclinical atherosclerosis in rheumatoid arthritis. Hypertens Res. 43(9):982-984 (2020). Bridges SL Jr, Niewold TB, Merriman TR. Is Rheumatoid Arthritis a Causal Factor in Cardiovascular Disease? 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Characteristics Total Tertiles of AIP T1(-0.53, -0.07) T2(-0.07, 0.11) T3(0.11, 1.03) P value N 1088 364 364 360 Sex, % 0.306 Female 856 (78.70%) 295 (81.04%) 278 (76.37%) 283 (78.61%) Male 232 (21.30%) 69 (18.96%) 86 (23.63%) 77 (21.39%) Age (year) 59.00 (51.00-68.00) 60.00 (49.00-68.00) 59.00 (51.00-69.00) 58.00 (51.00-66.00) 0.490 MH (month) 72.00 (12.00-132.00) 72.00 (21.00-144.00) 60.00 (12.00-120.00) 72.00 (12.00-130.75) 0.249 BMI (kg/m 2 ) 23.81±3.55 22.89 (20.70-24.97) 23.96±3.55 24.57±3.72 <0.001 SBP (mmHg) 132.00 (120.00-145.00) 130.00 (120.00-142.75) 132.00 (120.00-145.00) 133.13±15.97 0.236 DBP (mmHg) 80.00 (73.00-86.00) 80.00 (71.00-85.00) 80.00 (72.00-86.00) 80.00 (74.00-89.00) 0.007 Smoking, N (%) 0.185 No 1030 (94.70%) 351 (96.43%) 341 (93.68%) 338 (93.89%) Yes 58 (5.30%) 13 (3.57%) 23 (6.32%) 22 (6.11%) Alcohol use, N (%) 0.194 No 1069 (98.30%) 360 (98.90%) 354 (97.25%) 355 (98.61%) Yes 19 (1.70%) 4 (1.09%) 10 (2.75%) 5 (1.39%) Hypertension, N (%) 0.085 No 734 (67.50%) 261 (71.70%) 242 (66.48%) 231 (64.17%) Yes 354 (32.50%) 103 (28.30%) 122 (33.52%) 129 (35.83%) DM, N (%) 0.018 No 987 (90.70%) 343 (94.23%) 324 (89.01%) 320 (88.89%) Yes 101 (9.30%) 21 (5.77%) 40 (10.99%) 40 (11.11%) CHD, N (%) 0.157 No 1014 (93.20%) 343 (94.23%) 343 (94.23%) 328 (91.11%) Yes 74 (6.80%) 21 (94.23%) 21 (94.23%) 32 (8.89%) Antihyperlipidemic, N (%) 0.624 No 972 (89.3%) 327 (89.84%) 328 (90.11%) 317 (88.06%) Yes 116 (10.70%) 37 (10.16%) 36 (9.89%) 43 (11.94%) ESR (mm/H) 60.00 (35.00-88.00) 51.00 (30.00-86.00) 65.00 (39.00-90.00) 59.00 (37.00-87.00) 0.007 CRP (mg/L) 28.53 (7.45-48.97) 25.97 (6.06-47.81) 33.01 (10.62-53.09) 22.72 (6.60-45.88) 0.018 UA (umol/L) 249.00 (195.00-296.00) 224.50 (179.00-275.75) 248.18±73.86 264.00 (216.25-319.88) ＜0.001 FPG (mmol/L) 4.88 (4.52-5.35) 4.77 (4.45-5.15) 4.89 (4.53-5.45) 4.98 (4.60-5.45) ＜0.001 TC (mmol/L) 4.08 (3.48-4.69) 4.01 (3.45-4.57) 4.04 (3.46-4.67) 4.27±0.99 0.013 TG (mmol/L) 1.05 (0.83-1.41) 0.78 (0.67-0.91) 1.05 (0.92-1.20) 1.58 (1.31-1.97) <0.001 HDL-C (mmol/L) 1.02 (0.86-1.19) 1.19 (1.03-1.36) 1.01 (0.87-1.14) 0.87 (0.76-1.01) <0.001 LDL-C (mmol/L) 2.38 (1.95-2.90) 2.19 (1.80-2.65) 2.43 (1.97-2.90) 2.60±0.75 <0.001 DAS28-ESR 4.53 (3.92-5.06) 4.45 (3.72-4.94) 4.62 (4.01-5.10) 4.54 (3.98-5.08) 0.007 Abbreviations: AIP atherogenic index of plasma, MH medical history, BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, DM diabetes mellitus, CHD coronary heart disease, ESR erythrocyte sedimentation rate, CRP high-sensitivity C-reactive protein, UA uric acid, FPG fasting plasma glucose, TC total cholesterol, TG triglyceride, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol. Table 2. The results of univariate analysis. Characteristics Statistics DAS28-ESR β (95% CI) P value Sex, N (%) Female 856 (78.70%) Ref Male 232 (21.30%) -0.111 (-0.229, 0.008) 0.067 Age (year) 59.00 (51.00, 68.00) 0.012 (0.008, 0.016) <0.001 MH (month) 72.00 (12.00, 132.00) 0.000 (0.000, 0.001) 0.582 BMI (kg/m 2 ) 23.81±3.55 -0.005 (-0.018, 0.009) 0.502 SBP (mmHg) 132.00 (120.00, 145.00) -0.001 (-0.004, 0.002) 0.548 DBP (mmHg) 80.00 (73.00, 86.00) -0.004 (-0.009, 0.001) 0.089 Smoking, N (%) No 1030 (94.70%) Ref Yes 58 (5.30%) 0.104 (-0.113, 0.320) 0.348 Alcohol use, N (%) No 1069 (98.30%) Ref Yes 19 (1.70%) -0.037 (-0.408, 0.335) 0.847 Hypertension, N (%) No 734 (67.50%) Ref Yes 354 (32.50%) 0.109 (0.006, 0.213) 0.039 DM, N (%) No 987 (90.70%) Ref Yes 101 (9.30%) 0.170 (0.003, 0.337) 0.047 CHD, N (%) No 1014 (93.20%) Ref Yes 74 (6.80%) 0.014 (-0.180, 0.207) 0.891 Antihyperlipidemic, N (%) No 972 (89.3%) Ref Yes 116 (10.70%) 0.054 (-0.103, 0.212) 0.498 ESR (mm/H) 60.00 (35.00, 88.00) 0.019 (0.018, 0.020) <0.001 CRP (mg/L) 28.53 (7.45, 48.97) 0.009 (0.008, 0.010) <0.001 UA (umol/L) 249.00 (195.00, 296.00) 0.000 (0.000, 0.001) 0.535 FPG (mmol/L) 4.88 (4.52, 5.35) 0.061 (0.019, 0.102) 0.005 TC (mmol/L) 4.08 (3.48, 4.69) -0.083 (-0.134, -0.032) 0.001 TG (mmol/L) 1.05 (0.83, 1.41) -0.121 (-0.196, -0.047) 0.001 HDL-C (mmol/L) 1.02 (0.86, 1.19) -0.804 (-0.984, -0.623) <0.001 LDL-C (mmol/L) 2.38 (1.95, 2.90) 0.008(-0.059, 0.075) 0.812 AIP 0.02 (-0.12, 0.16) 0.262 (0.039, 0.486) 0.022 Abbreviations : MH medical history, BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, DM diabetes mellitus, CHD coronary heart disease, ESR erythrocyte sedimentation rate, CRP high-sensitivity C-reactive protein, UA uric acid, FPG fasting plasma glucose, TC total cholesterol, TG triglyceride, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, AIP atherogenic index of plasma. Table 3. Multivariate linear regression results of association between AIP and DAS28-ESR. Exposure Crude model β (95% CI) Adjust I β (95% CI) Adjust II β (95% CI) DAS28-ESR AIP 0.262 (0.039, 0.486) 0.320 (0.095, 0.546) 0.312 (0.080, 0.545) AIP (Tertiles) T1 Ref Ref Ref T2 0.166 (0.047, 0.284) 0.163 (0.045, 0.280) 0.159 (0.041, 0.277) T3 0.178 (0.059, 0.297) 0.192 (0.073, 0.311) 0.192 (0.071, 0.314) P for trend 0.003 0.002 0.002 Crude model adjust for: None. Adjust I model adjust for: sex; age; BMI. Adjust II model adjust for: sex; age; BMI; hypertension; diabetes mellitus; coronary heart disease; antihyperlipidemic; uric acid; fasting plasma glucose. Abbreviation s : AIP atherogenic index of plasma. Table 4. The result of two-piecewise linear regression model of AIP with DAS28-ESR. DAS28-ESR β (95% CI) Fitting model by standard linear regression 4.434 (4.386, 4.482) Fitting model by two-piecewise linear regression Inflection points of AIP 0.175 Inflection point -1.010 (-1.682, -0.339) P for log likelihood ratio test <0.001 Note: The models were adjusted for: sex; age; BMI; hypertension; diabetes mellitus; coronary heart disease; antihyperlipidemic; uric acid; fasting plasma glucose. Abbreviation s: AIP atherogenic index of plasma. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7722454","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":601479871,"identity":"a7998ea9-1bbd-4f87-bde9-f240ed1ee1ae","order_by":0,"name":"Lina Leng","email":"","orcid":"","institution":"Xingtai People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lina","middleName":"","lastName":"Leng","suffix":""},{"id":601479879,"identity":"faff365f-683f-429c-a9c9-37580327c285","order_by":1,"name":"Ying Li","email":"","orcid":"","institution":"82 Group Hospital of Chinese People's Liberation Army","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Li","suffix":""},{"id":601479883,"identity":"d4f457fc-5552-4c86-9463-8a93c4404b60","order_by":2,"name":"Quanyi Tang","email":"","orcid":"","institution":"Graduate School of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Quanyi","middleName":"","lastName":"Tang","suffix":""},{"id":601479886,"identity":"eb41a13d-e6e1-4a1a-bf34-506a9d4de9e2","order_by":3,"name":"Jinfeng Zhang","email":"","orcid":"","institution":"Xingtai People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jinfeng","middleName":"","lastName":"Zhang","suffix":""},{"id":601479890,"identity":"d3085f5f-ce17-435c-a1f9-2099d9a1132e","order_by":4,"name":"Yaorong Han","email":"","orcid":"","institution":"Xingtai People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yaorong","middleName":"","lastName":"Han","suffix":""},{"id":601479893,"identity":"fde73c31-4d06-4ea7-9273-4d5f2f44f704","order_by":5,"name":"Xiaoli Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIie3QoQrCQBzH8f/4w3/l2ILFIfgMfxkMw9izOAZnMdgNzmLyAfYYPsLm4SzDLGgQBPPAYjA41KbM2Qz3LcfBfcLvAHS6fyzFOAUGsBCzY8l+0IAYT0JIUS8Zy6gReZwEwmuJcvW61mTts5kS46BLJkn0OUUw1XpZR5xtGGcJRy4h5qcRHywQUu7qCBfVlpIxnCMN3RGfEdrC+04GPK2I8Dp9VkbciJSsngSaEKci1ZaN+/jkBcuIvm2xClQXcZt0bVtlx+vND2xT5bXkPfrtuU6n0+k+dQfwKUkMl2HTAgAAAABJRU5ErkJggg==","orcid":"","institution":"Xingtai People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Xiaoli","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-09-26 13:53:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7722454/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7722454/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104204384,"identity":"6202fce5-b7ec-4418-8c1d-ddeb1aad64cd","added_by":"auto","created_at":"2026-03-09 06:33:20","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48746,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participant selection.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7722454/v1/ccda5d01d6ed4c2a8f771e7f.jpg"},{"id":104405088,"identity":"a9c471d8-6095-4ee8-b414-157722b7f774","added_by":"auto","created_at":"2026-03-11 12:21:43","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":177993,"visible":true,"origin":"","legend":"\u003cp\u003eA nonlinear relationship of plasma atherogenic index with DAS28-ESR. Note: The model was adjusted for sex; age; BMI; hypertension; diabetes mellitus; coronary heart disease; antihyperlipidemic; uric acid; fasting plasma glucose.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7722454/v1/eb6b9132d2fffd89a8c5cd1c.jpg"},{"id":105805844,"identity":"eed4a16b-95ad-42cd-aa86-306e536ca65b","added_by":"auto","created_at":"2026-03-31 10:27:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":967485,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7722454/v1/81d9ef67-2a64-4b26-87cb-35484996bbbd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Non-linear Association between Atherogenic Index of Plasma and Disease Activity in Rheumatoid Arthritis: A Cross-sectional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRheumatoid arthritis (RA) is a common systemic autoimmune disease characterized by chronic synovitis and progressive joint destruction, causing significant disability burden and decreased quality of life worldwide\u003csup\u003e1\u003c/sup\u003e. In addition to the limitation of musculoskeletal system, RA is clearly considered as an independent risk factor for accelerating atherosclerosis and premature cardiovascular disease (CVD), which is the main cause of death in this patient group\u003csup\u003e2,3\u003c/sup\u003e. This complex connection is largely coordinated by a sustained systemic inflammatory state, in which circulating pro-inflammatory cytokines, especially tumor necrosis factor alpha (TNF - \u0026alpha;) and interleukin-6 (IL-6), not only cause joint damage, but also trigger endothelial dysfunction, promote plaque instability, and disrupt metabolic homeostasis\u003csup\u003e4,5\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eEvaluating the cardiovascular risk of RA requires a multifaceted approach that combines traditional risk factors with disease-specific parameters. Among them, dyslipidemia plays a crucial but contradictory and complex role. The classic lipid mass spectrum of active RA usually shows a \u0026quot;lipid paradox\u0026quot;, characterized by low levels of total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C), which is in sharp contrast to the typical atherogenic lipid model common in the general population\u003csup\u003e6,7\u003c/sup\u003e. This phenomenon is attributed to the increased inflammatory cascade reaction leading to lipid consumption, which complicates the interpretation of traditional lipid parameters and may underestimate the real risk of atherosclerosis\u003csup\u003e8\u003c/sup\u003e. Therefore, there is an urgent need for a more robust and anti-inflammatory lipid index in clinical practice to accurately reflect cardiovascular risk under autoimmune background.\u003c/p\u003e\n\u003cp\u003eThe plasma atherosclerosis index (AIP) is calculated by the base logarithm of 10 of the molar ratio of triglyceride to high-density lipoprotein cholesterol (log (TG/HDL-C)), which has become an effective and comprehensive biomarker of atherosclerotic dyslipidemia\u003csup\u003e9\u003c/sup\u003e. Unlike individual lipid measurements, AIP is closely associated with the presence of small and dense LDL particles, which are a subtype highly sensitive to oxidation and have good arterial intima permeability. They are also closely related to insulin resistance and systemic inflammation\u003csup\u003e10,11\u003c/sup\u003e. Its prognostic value for cardiovascular events has been verified in metabolic syndrome, diabetes and the general population\u003csup\u003e12,13\u003c/sup\u003e. Given that the TG/HDL-C ratio reflects pro-inflammatory and pro thrombotic states, AIP can serve as a mirror to capture the interaction between lipid metabolism and inflammatory burden in RA. Therefore, this study utilized data from 1088 RA patients to comprehensively elucidate the association between AIP and RA disease activity, provide a more complex and detailed understanding of the lipid inflammatory axis of RA, and provide information for future targeted therapy strategies.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003e\u003cstrong\u003eResearch Design and Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a cross-sectional study that included 1088 consecutive RA patients who visited Xingtai People\u0026apos;s Hospital. All patients met the RA classification criteria proposed by the American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) in 2010\u003csup\u003e14\u003c/sup\u003e. Exclusion criteria include: severe liver and kidney dysfunction, malignant tumors, and incomplete data. The screening process for research participants is shown in Figure 1. This study was approved by the Ethics Committee of our hospital (batch number: 2025 [031]), and all patients signed informed consent forms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCollect demographic data, clinical characteristics, and laboratory indicators of patients through an electronic medical record system. Including: gender, age, course of disease (MH), body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), smoking history, drinking history, hypertension history, diabetes mellitus (DM) history, coronary heart disease (CHD) history, and use of lipid-lowering drugs. Laboratory indicators include: erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), uric acid (UA), fasting blood glucose (FPG), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C). AIP is calculated using the formula: AIP=log (TG/HDL-C). Disease activity was evaluated using the 28 joint disease activity score DAS28-ESR score.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the third quartile of AIP, all patients were divided into three groups: T1, T2, and T3. If the measurement data conforms to a normal distribution, it is expressed as mean \u0026plusmn; standard deviation, and analysis of variance is used for inter group comparison; If it does not follow a normal distribution, it is represented by the median (interquartile range), and the Kruskal Wallis H test is used for inter group comparison. Count data is presented in terms of examples (percentage), and comparison between groups is conducted using chi square test. Preliminary screening of variables related to DAS28-ESR using single factor linear regression. Subsequently, a multiple linear regression model was constructed to analyze the association between AIP and DAS28-ESR. We established three models: crude model (unadjusted variables), adjusted model I (adjusted for gender, age, BMI) and adjusted model II (further adjusted for hypertension, DM, CHD, use of lipid-lowering drugs, UA, and FPG). To explore nonlinear relationships, we use a two-stage linear regression model and calculate the inflection point. Compare the goodness of fit between the piecewise linear model and the standard linear model using a log likelihood ratio test. All statistical analyses were conducted using R software (version 4.0.3) and SPSS (version 26.0). P\u0026lt;0.05 indicates that the difference is statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 1088 RA patients were included in this study, and their baseline characteristics were grouped according to AIP quartiles as shown in Table 1. There were significant differences in BMI, DBP, diabetes history, ESR, CRP, UA, FPG, TC, TG, HDL-C, LDL-C and DAS28-ESR among the three groups (P\u0026lt;0.05). As AIP levels increase (from T1 to T3), TG, FPG, TC, LDL-C, UA levels, and DAS28-ESR scores show an upward trend, while HDL-C levels significantly decrease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle factor analysis affecting DAS28-ESR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of single factor linear regression analysis (Table 2) showed that age (β=0.012, P\u0026lt;0.001), hypertension (β=0.109, P=0.039), diabetes (β=0.170, P=0.047), ESR (β=0.019, P\u0026lt;0.001), CRP (β=0.009, P\u0026lt;0.001), FPG (β=0.061, P=0.005) were positively correlated with DAS28-ESR. But TC（β=-0.083, P=0.001）、TG（β=-0.121, P=0.001）、HDL-C（β=-0.804, P\u0026lt;0.001）\u0026nbsp;It is negatively correlated with DAS28-ESR. It is worth noting that AIP is significantly positively correlated with DAS28-ESR (β=0.262, P=0.022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate linear regression analysis of AIP and DAS28-ESR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 3, in the unadjusted coarse model, for every unit increase in AIP, the DAS28-ESR score significantly increased by 0.262 units (95% CI: 0.039~0.486). After gradually adjusting for gender, age, BMI (Model I), and other metabolic confounding factors (Model II), this positive correlation remained significant, and the effect value even slightly increased (adjusted Model II: β=0.312, 95% CI: 0.080-0.545). After analyzing AIP as a categorical variable (triplet array), the results showed that compared with the T1 group, the DAS28-ESR scores of patients in the T2 and T3 groups were significantly higher, and there was a significant dose-response trend (trend P-value=0.002).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNonlinear Relationship Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the complexity of lipid metabolism, we further investigated whether there is a nonlinear correlation between AIP and DAS28-ESR. The two-stage linear regression model (Table 4) revealed a significant nonlinear relationship (log likelihood ratio test P\u0026lt;0.001), with an inflection point of AIP=0.175. Within the range of AIP\u0026lt;0.175, there is a strong positive correlation between AIP and DAS28-ESR (β=0.927, 95% CI: 0.543-1.311); However, when AIP\u0026gt;0.175, the relationship between the two reverses and shows a negative correlation (β=-1.010, 95% CI: -1.682~-0.339). See Figure 2.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe confirmed that among 1088 RA patients, higher AIP was associated with increased disease activity, and this association still existed after strict adjustment for confounding factors. Secondly, and most interestingly, our analysis using a two-stage linear regression model revealed a significant nonlinear relationship characterized by a critical inflection point at AIP=0.175. Below this threshold, AIP is strongly positively correlated with DAS28-ESR; Surprisingly, beyond this point, this relationship reversed and showed a negative correlation. These findings deepen our understanding of the complex interactions between lipid metabolism and disease activity in RA.\u003c/p\u003e\n\u003cp\u003eAIP is widely considered as a reliable marker of dyslipidemia and atherosclerosis. Multiple studies have shown that both baseline and cumulative AIP exposure are associated with cardiovascular disease, particularly coronary artery disease\u003csup\u003e15,16\u003c/sup\u003e. In addition, studies have found that higher levels of AIP are positively correlated with the risk of hypertension and non-alcoholic fatty liver disease\u003csup\u003e17,18\u003c/sup\u003e. A cross-sectional study shows that there is a close relationship between the increased AIP level, the increased risk of insulin resistance and the onset of type 2 diabetes\u003csup\u003e19\u003c/sup\u003e. Recent studies have explored the relationship between AIP and cerebrovascular diseases, indicating that a higher level of AIP is associated with a higher incidence of carotid and intracranial atherosclerotic stenosis\u003csup\u003e20,21\u003c/sup\u003e. In terms of RA disease, more research has focused on the relationship between AIP and the risk of developing cardiovascular disease, indicating that AIP can serve as an independent indicator of long-term CVD risk in RA patients\u003csup\u003e22\u003c/sup\u003e. Further investigation is needed to determine whether AIP levels can serve as a biomarker for disease activity in RA patients. To our knowledge, this study provides the first evidence of a non-linear correlation between baseline AIP levels and disease activity in RA patients.\u003c/p\u003e\n\u003cp\u003eOur initial linear model is consistent with an increasing number of literature indicating that dyslipidemia is associated with the inflammatory pathogenesis of RA\u003csup\u003e23,24\u003c/sup\u003e. The positive correlation between AIP and DAS28-ESR, even after adjusting the confounding factors, also strengthens the concept that atherosclerotic dyslipidemia is not only a complication, but also may have an internal relationship with disease activity. Mechanistically, this relationship can be conceptualized through several pathways. Research has shown that the levels of small and dense low-density lipoprotein (sdLDL) are significantly elevated in RA patients, and the ability of LDL to specifically bind to chondroitin sulfate proteoglycans on the surface of macrophages is enhanced\u003csup\u003e25,26\u003c/sup\u003e. In addition, sdLDL can promote the growth of atherosclerosis by regulating lipid metabolism, inducing inflammation and enhancing endothelial damage\u003csup\u003e27\u003c/sup\u003e. It is worth noting that smaller sdLDL particles are more likely to form oxidized LDL, penetrate target cells, and are not easily cleared\u003csup\u003e28\u003c/sup\u003e. Because of this, the LDL receptor is unable to recognize sdLDL, making it easier to be absorbed by macrophages and become foam cells, thus promoting the development and occurrence of atherosclerosis\u003csup\u003e29\u003c/sup\u003e. Simultaneously oxidized LDL can also activate synovial fibroblasts and innate immune cells through scavenger receptors, making pro-inflammatory cytokine cascades (such as IL-1 \u0026beta;, IL-6, TNF - \u0026alpha;) permanent, thereby exacerbating systemic inflammation and joint damage\u003csup\u003e30\u003c/sup\u003e. In addition, insulin resistance (AIP is a recognized alternative biomarker) can lead to a pro-inflammatory state characterized by elevated levels of free fatty acids and adipokines, further exacerbating the progression of RA disease\u003csup\u003e31\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eHowever, the most provocative finding in our study was that the inversion of the AIP-DAS28 relationship exceeded the threshold of 0.175. This phenomenon can be explained by the well described \u0026apos;lipid paradox\u0026apos; in RA and other chronic inflammatory states. We hypothesize a biphasic model of lipid inflammation interaction: in the mild to moderate inflammatory stage (usually corresponding to low to moderate AIP levels), positive correlation dominates. Here, metabolic abnormalities drive inflammation through the aforementioned mechanisms. On the contrary, in severe and uncontrolled inflammatory states (which may occur simultaneously with very high AIP levels), the classic lipid paradox becomes apparent. High levels of systemic cytokines can significantly inhibit lipoprotein lipase activity, leading to hypertriglyceridemia, while inhibiting lecithin cholesterol acyltransferase (LCAT) enzyme, reducing the maturity and function of HDL-C\u003csup\u003e32\u003c/sup\u003e. More importantly, rampant inflammatory reactions may lead to the depletion of lipid components, which are used as energy substrates by overactive immune cells or incorporated into cell membranes during rapid immune cell proliferation\u003csup\u003e33\u003c/sup\u003e. This consumption effect may paradoxically lead to a relative decrease in measured disease activity, which explains the negative slope observed at very high AIP levels. Therefore, this inflection point (AIP=0.175) may represent a critical metabolic inflammatory threshold.\u003c/p\u003e\n\u003cp\u003eOur research must be explained within the context of its limitations. The cross-sectional design excludes any inference of causal relationships; We cannot determine whether AIP triggers inflammation, whether inflammation alters lipid metabolism, or whether both processes are influenced by a third unmeasured factor. Although we have adjusted for various confounding factors, residual confounding factors are still a possibility. For example, there is no detailed data available for adjusting dietary habits and physical activity levels. Finally, as a single center study, the generalizability of our findings, including specific inflection point values, needs to be validated in larger multicenter prospective cohorts.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, our research indicates that the relationship between AIP and RA disease activity is not linear, but rather has a significant threshold effect. Future prospective research is crucial for verifying this turning point and exploring the potential mechanisms driving this biphasic relationship. Ultimately, understanding this complex interaction can provide a basis for more personalized management strategies targeting both inflammation and lipid metabolism in RA patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data will be shared on reasonable request to the corresponding or the senior author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author thanks Xingtai People's Hospital for providing data on this platform and thanks all participants for their selfless dedication.\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\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLL and XL proposed the strategy, LL conducted the data extraction and organization as well as the final draft, QT provided statistical methodological guidance, LL, YL, JZ and YH analyzed and validated the data, and QT and YL produced the charts. All authors conducted the review and approved the final manuscript. LL and XL are the guarantors of this work and are responsible for the completeness and accuracy of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Key R\u0026amp;D Projects in Xingtai City (No. 2025ZC074).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received approval from the Research Ethics Committee of Xingtai People’s Hospital (approval number: 2025[031]). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMcInnes IB, Schett G. The pathogenesis of rheumatoid arthritis. N Engl J Med. 8;365(23):2205-19 (2011).\u003c/li\u003e\n\u003cli\u003eEngland BR, Thiele GM, Anderson DR, Mikuls TR. Increased cardiovascular risk in rheumatoid arthritis: mechanisms and implications. BMJ. 23;361:k1036 (2018). \u003c/li\u003e\n\u003cli\u003eHannawi S, Hannawi H, Al Salmi I. Cardiovascular disease and subclinical atherosclerosis in rheumatoid arthritis. Hypertens Res. 43(9):982-984 (2020).\u003c/li\u003e\n\u003cli\u003eBridges SL Jr, Niewold TB, Merriman TR. Is Rheumatoid Arthritis a Causal Factor in Cardiovascular Disease? Arthritis Rheumatol. 74(10):1612-1614 (2022). \u003c/li\u003e\n\u003cli\u003eSemb AG, Ikdahl E, Wibetoe G, Crowson C, Rollefstad S. Atherosclerotic cardiovascular disease prevention in rheumatoid arthritis. Nat Rev Rheumatol. 16(7):361-379 (2020). \u003c/li\u003e\n\u003cli\u003eYan J, et al. Dyslipidemia in rheumatoid arthritis: the possible mechanisms. Front Immunol. 25;14:1254753 (2023). \u003c/li\u003e\n\u003cli\u003eBehl T, et al. The Lipid Paradox as a Metabolic Checkpoint and Its Therapeutic Significance in Ameliorating the Associated Cardiovascular Risks in Rheumatoid Arthritis Patients. Int J Mol Sci. 14;21(24):9505 (2020). \u003c/li\u003e\n\u003cli\u003eVenetsanopoulou AI, Pelechas E, Voulgari PV, Drosos AA. The lipid paradox in rheumatoid arthritis: the dark horse of the augmented cardiovascular risk. Rheumatol Int. 40(8):1181-1191 (2020). \u003c/li\u003e\n\u003cli\u003eYou FF, et al. Association between atherogenic index of plasma and all-cause mortality and specific-mortality: a nationwide population based cohort study. Cardiovasc Diabetol. 27;23(1):276 (2024). \u003c/li\u003e\n\u003cli\u003eHermans MP, Ahn SA, Rousseau MF. log(TG)/HDL-C is related to both residual cardiometabolic risk and \u0026beta;-cell function loss in type 2 diabetes males. Cardiovasc Diabetol. 9:88 (2010). \u003c/li\u003e\n\u003cli\u003eHoogeveen RC, Ballantyne CM. Residual cardiovascular risk at low LDL: remnants, lipoprotein(a), and inflammation. Clin Chem. 67(1):143\u0026ndash;153 (2021). \u003c/li\u003e\n\u003cli\u003eAndraschko LM, Gazi G, Leucuta DC, Popa SL, Chis BA, Ismaiel A. Atherogenic Index of Plasma in Metabolic Syndrome-A Systematic Review and Meta-Analysis. Medicina (Kaunas). 27;61(4):611 (2025). \u003c/li\u003e\n\u003cli\u003eJiang L, et al. Non-linear associations of atherogenic index of plasma with prediabetes and type 2 diabetes mellitus among Chinese adults aged 45 years and above: a cross-sectional study from CHARLS. Front Endocrinol (Lausanne). 2;15:1360874 (2024). \u003c/li\u003e\n\u003cli\u003eAletaha D, et al. 2010 Rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Arthritis Rheum. 62(9):2569-81 (2010). \u003c/li\u003e\n\u003cli\u003eFern\u0026aacute;ndez-Mac\u0026iacute;as JC, Ochoa-Mart\u0026iacute;nez AC, Varela-Silva JA, P\u0026eacute;rez-Maldonado IN. Atherogenic index of plasma: Novel Predictive Biomarker for Cardiovascular illnesses. Arch Med Res. 50(5):285\u0026ndash;94 (2019). \u003c/li\u003e\n\u003cli\u003eKim SH, et al. Association of the atherogenic index of plasma with cardiovascular risk beyond the traditional risk factors: a nationwide population-based cohort study. Cardiovasc Diabetol. 21(1):81 (2022). \u003c/li\u003e\n\u003cli\u003eTan M, et al. Association between atherogenic index of plasma and prehypertension or hypertension among normoglycemia subjects in a Japan population: a cross-sectional study. Lipids Health Dis. 22(1):87 (2023). \u003c/li\u003e\n\u003cli\u003eLi K, Li J, Cheng X, Wang J, Li J. Association between the atherogenic index of plasma and new-onset non-alcoholic fatty liver disease in non-obese participants. Front Endocrinol. 13:969783 (2022). \u003c/li\u003e\n\u003cli\u003eYin B, Wu Z, Xia Y, Xiao S, Chen L, Li Y. Non-linear association of atherogenic index of plasma with insulin resistance and type 2 diabetes: a cross-sectional study. Cardiovasc Diabetol. 22(1):157 (2023). \u003c/li\u003e\n\u003cli\u003eYu S, et al. The predictive value of nontraditional lipid parameters for intracranial and extracranial atherosclerotic stenosis: a hospital-based observational study in China. Lipids Health Dis. 22(1):16 (2023). \u003c/li\u003e\n\u003cli\u003eHuang Q, et al. The atherogenic index of plasma and carotid atherosclerosis in a community population: a population-based cohort study in China. Cardiovasc Diabetol. 22(1):125 (2023). \u003c/li\u003e\n\u003cli\u003eHammam N, Abdel-Wahab N, Gheita TA. Atherogenic Index of Plasma in Women with Rheumatoid Arthritis and Systemic Lupus Erythematosus: A 10-Year Potential Predictor of Cardiovascular Disease. Curr Rheumatol Rev. 17(1):122-130 (2021). \u003c/li\u003e\n\u003cli\u003eBag-Ozbek A, Giles JT. Inflammation, adiposity, and atherogenic dyslipidemia in rheumatoid arthritis: is there a paradoxical relationship? Curr Allergy Asthma Rep. 15(2):497 (2015). \u003c/li\u003e\n\u003cli\u003eNurmohamed MT, Dijkmans BA. Dyslipidaemia, statins and rheumatoid arthritis. Ann Rheum Dis. 68(4):453-5 (2009). \u003c/li\u003e\n\u003cli\u003eRizzo M, Spinas GA, Cesur M, Ozbalkan Z, Rini GB, Berneis K. Atherogenic lipoprotein phenotype and LDL size and subclasses in drug-na\u0026iuml;ve patients with early rheumatoid arthritis. Atherosclerosis 207:502\u0026ndash;6 (2009).\u003c/li\u003e\n\u003cli\u003eFilippatos TD, et al. Effects of 12 months of treatment with disease-modifying anti-rheumatic drugs on low and high density lipoprotein subclass distribution in patients with early rheumatoid arthritis: a pilot study. Scand J Rheumatol. 42:169\u0026ndash;75 (2013). \u003c/li\u003e\n\u003cli\u003eJin X, Yang S, Lu J, Wu M. Small, dense low-density lipoprotein-cholesterol and atherosclerosis: relationship and therapeutic strategies. Front Cardiovasc Med. 8:804214 (2021). \u003c/li\u003e\n\u003cli\u003eNesto RW. Beyond low-density lipoprotein: addressing the atherogenic lipid triad in type 2 diabetes mellitus and the metabolic syndrome. Am J Cardiovasc Drugs. 5:379\u0026ndash;87 (2005). \u003c/li\u003e\n\u003cli\u003eChapman MJ, et al. LDL subclass lipidomics in atherogenic dyslipidemia: effect of statin therapy on bioactive lipids and dense LDL. J Lipid Res. 61:911\u0026ndash;32 (2020). \u003c/li\u003e\n\u003cli\u003eRizzo M, Spinas GA, Cesur M, Ozbalkan Z, Rini GB, Berneis K. Atherogenic lipoprotein phenotype and LDL size and subclasses in drug-na\u0026iuml;ve patients with early rheumatoid arthritis. Atherosclerosis. 207(2):502-6 (2009). \u003c/li\u003e\n\u003cli\u003eNicolau J, Lequerr\u0026eacute; T, Bacquet H, Vittecoq O. Rheumatoid arthritis, insulin resistance, and diabetes. Joint Bone Spine. 84(4):411-416 (2017). \u003c/li\u003e\n\u003cli\u003eVenetsanopoulou AI, Pelechas E, Voulgari PV, Drosos AA. The lipid paradox in rheumatoid arthritis: the dark horse of the augmented cardiovascular risk. Rheumatol. Int. 40:1181\u0026ndash;91 (2020). \u003c/li\u003e\n\u003cli\u003eMyasoedova E, Crowson CS, Kremers HM, Fitz-Gibbon PD, Therneau TM, Gabriel SE. Total cholesterol and LDL levels decrease before rheumatoid arthritis. Ann Rheum Dis. 69:1310\u0026ndash;4 (2010). \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. General feature description according to tertiles of AIP.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"630\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 402px;\"\u003e\n \u003cp\u003eTertiles of AIP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eT1(-0.53, -0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eT2(-0.07, 0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eT3(0.11, 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 569px;\"\u003e\n \u003cp\u003eSex, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e856 (78.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e295 (81.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e278 (76.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e283 (78.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e232 (21.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e69 (18.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e86 (23.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e77 (21.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e59.00 (51.00-68.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e60.00 (49.00-68.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e59.00 (51.00-69.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e58.00 (51.00-66.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eMH (month)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e72.00 (12.00-132.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e72.00 (21.00-144.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e60.00 (12.00-120.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e72.00 (12.00-130.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e23.81\u0026plusmn;3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e22.89 (20.70-24.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e23.96\u0026plusmn;3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e24.57\u0026plusmn;3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e132.00 (120.00-145.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e130.00 (120.00-142.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e132.00 (120.00-145.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e133.13\u0026plusmn;15.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e80.00 (73.00-86.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e80.00 (71.00-85.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e80.00 (72.00-86.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e80.00 (74.00-89.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 569px;\"\u003e\n \u003cp\u003eSmoking, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1030 (94.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e351 (96.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e341 (93.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e338 (93.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e58 (5.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e13 (3.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e23 (6.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e22 (6.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 569px;\"\u003e\n \u003cp\u003eAlcohol use, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1069 (98.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e360 (98.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e354 (97.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e355 (98.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e19 (1.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e4 (1.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e10 (2.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e5 (1.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 458px;\"\u003e\n \u003cp\u003eHypertension, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e734 (67.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e261 (71.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e242 (66.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e231 (64.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e354 (32.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e103 (28.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e122 (33.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e129 (35.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 569px;\"\u003e\n \u003cp\u003eDM, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e987 (90.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e343 (94.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e324 (89.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e320 (88.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e101 (9.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e21 (5.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e40 (10.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e40 (11.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 569px;\"\u003e\n \u003cp\u003eCHD, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1014 (93.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e343 (94.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e343 (94.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e328 (91.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e74 (6.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e21 (94.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e21 (94.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e32 (8.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 569px;\"\u003e\n \u003cp\u003eAntihyperlipidemic, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e972 (89.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e327 (89.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e328 (90.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e317 (88.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e116 (10.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e37 (10.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e36 (9.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e43 (11.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eESR (mm/H)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e60.00 (35.00-88.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e51.00 (30.00-86.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e65.00 (39.00-90.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e59.00 (37.00-87.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eCRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e28.53 (7.45-48.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e25.97 (6.06-47.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e33.01 (10.62-53.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e22.72 (6.60-45.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eUA (umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e249.00 (195.00-296.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e224.50 (179.00-275.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e248.18\u0026plusmn;73.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e264.00 (216.25-319.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eFPG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e4.88 (4.52-5.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e4.77 (4.45-5.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e4.89 (4.53-5.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e4.98 (4.60-5.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e4.08 (3.48-4.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e4.01 (3.45-4.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e4.04 (3.46-4.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e4.27\u0026plusmn;0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1.05 (0.83-1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.78 (0.67-0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.05 (0.92-1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e1.58 (1.31-1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1.02 (0.86-1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e1.19 (1.03-1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.01 (0.87-1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.87 (0.76-1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2.38 (1.95-2.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e2.19 (1.80-2.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.43 (1.97-2.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2.60\u0026plusmn;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eDAS28-ESR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e4.53 (3.92-5.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e4.45 (3.72-4.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e4.62 (4.01-5.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e4.54 (3.98-5.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003e\u003cem\u003eAIP\u003c/em\u003e atherogenic index of plasma, \u003cem\u003eMH\u003c/em\u003e medical history, \u003cem\u003eBMI\u003c/em\u003e body mass index, \u003cem\u003eSBP\u0026nbsp;\u003c/em\u003esystolic blood pressure, \u003cem\u003eDBP\u0026nbsp;\u003c/em\u003ediastolic blood pressure, \u003cem\u003eDM\u003c/em\u003e diabetes mellitus, \u003cem\u003eCHD\u003c/em\u003e coronary heart disease, \u003cem\u003eESR\u003c/em\u003e erythrocyte sedimentation rate, \u003cem\u003eCRP\u003c/em\u003e high-sensitivity C-reactive protein, \u003cem\u003eUA\u003c/em\u003e uric acid, \u003cem\u003eFPG\u003c/em\u003e fasting plasma glucose, \u003cem\u003eTC\u003c/em\u003e total cholesterol, \u003cem\u003eTG\u003c/em\u003e triglyceride, \u003cem\u003eHDL-C\u003c/em\u003e high-density lipoprotein cholesterol, \u003cem\u003eLDL-C\u003c/em\u003e low-density lipoprotein cholesterol.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e The results of univariate analysis.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"524\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eStatistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eDAS28-ESR \u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 448px;\"\u003e\n \u003cp\u003eSex, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e856 (78.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e232 (21.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e-0.111 (-0.229, 0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eAge\u0026nbsp;(year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e59.00 (51.00, 68.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.012 (0.008, 0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eMH\u0026nbsp;(month)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e72.00 (12.00, 132.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.000 (0.000, 0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eBMI\u0026nbsp;(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e23.81\u0026plusmn;3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e-0.005 (-0.018, 0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e132.00 (120.00, 145.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e-0.001 (-0.004, 0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e80.00 (73.00, 86.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e-0.004 (-0.009, 0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 448px;\"\u003e\n \u003cp\u003eSmoking, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e1030 (94.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e58 (5.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.104 (-0.113, 0.320)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 448px;\"\u003e\n \u003cp\u003eAlcohol use, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e1069 (98.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e19 (1.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e-0.037 (-0.408, 0.335)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 448px;\"\u003e\n \u003cp\u003eHypertension, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e734 (67.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e354 (32.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.109 (0.006, 0.213)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 448px;\"\u003e\n \u003cp\u003eDM, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e987 (90.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e101 (9.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.170 (0.003, 0.337)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 448px;\"\u003e\n \u003cp\u003eCHD, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e1014 (93.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e74 (6.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.014 (-0.180, 0.207)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 448px;\"\u003e\n \u003cp\u003eAntihyperlipidemic, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e972 (89.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e116 (10.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.054 (-0.103, 0.212)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.498\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eESR (mm/H)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e60.00 (35.00, 88.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.019 (0.018, 0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eCRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e28.53 (7.45, 48.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.009 (0.008, 0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eUA (umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e249.00 (195.00, 296.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.000 (0.000, 0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eFPG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e4.88 (4.52, 5.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.061 (0.019, 0.102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e4.08 (3.48, 4.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e-0.083 (-0.134, -0.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e1.05 (0.83, 1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e-0.121 (-0.196, -0.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e1.02 (0.86, 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e-0.804 (-0.984, -0.623)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e2.38 (1.95, 2.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.008(-0.059, 0.075)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eAIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.02 (-0.12, 0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.262 (0.039, 0.486)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e:\u0026nbsp;\u003cem\u003eMH\u003c/em\u003e medical history, \u003cem\u003eBMI\u003c/em\u003e body mass index, \u003cem\u003eSBP\u0026nbsp;\u003c/em\u003esystolic blood pressure, \u003cem\u003eDBP\u0026nbsp;\u003c/em\u003ediastolic blood pressure, \u003cem\u003eDM\u003c/em\u003e diabetes mellitus, \u003cem\u003eCHD\u003c/em\u003e coronary heart disease, \u003cem\u003eESR\u003c/em\u003e erythrocyte sedimentation rate, \u003cem\u003eCRP\u003c/em\u003e high-sensitivity C-reactive protein, \u003cem\u003eUA\u003c/em\u003e uric acid, \u003cem\u003eFPG\u003c/em\u003e fasting plasma glucose, \u003cem\u003eTC\u003c/em\u003e total cholesterol, \u003cem\u003eTG\u003c/em\u003e triglyceride, \u003cem\u003eHDL-C\u003c/em\u003e high-density lipoprotein cholesterol, \u003cem\u003eLDL-C\u003c/em\u003e low-density lipoprotein cholesterol,\u0026nbsp;\u003cem\u003eAIP\u003c/em\u003e atherogenic index of plasma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Multivariate linear regression results of association between AIP and DAS28-ESR.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"587\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003eCrude model\u003c/p\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eAdjust I\u003c/p\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eAdjust II\u003c/p\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 587px;\"\u003e\n \u003cp\u003eDAS28-ESR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eAIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e0.262 (0.039, 0.486)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.320 (0.095, 0.546)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.312 (0.080, 0.545)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 587px;\"\u003e\n \u003cp\u003eAIP (Tertiles)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e0.166 (0.047, 0.284)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.163 (0.045, 0.280)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.159 (0.041, 0.277)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e0.178 (0.059, 0.297)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.192 (0.073, 0.311)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.192 (0.071, 0.314)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCrude model adjust for: None.\u003c/p\u003e\n\u003cp\u003eAdjust I model adjust for: sex; age; BMI.\u003c/p\u003e\n\u003cp\u003eAdjust II model adjust for: sex; age; BMI; hypertension; diabetes mellitus; coronary heart disease; antihyperlipidemic; uric acid; fasting plasma glucose.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e:\u0026nbsp;\u003cem\u003eAIP\u003c/em\u003e atherogenic index of plasma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eThe result of two-piecewise linear regression model of AIP with DAS28-ESR.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"529\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003eDAS28-ESR\u003c/p\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003eFitting model by standard linear regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e4.434 (4.386, 4.482)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 529px;\"\u003e\n \u003cp\u003eFitting model by two-piecewise linear regression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003eInflection points of AIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003e\u0026lt;Inflection point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e0.927 (0.543, 1.311)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003e\u0026gt;Inflection point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e-1.010 (-1.682, -0.339)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for log likelihood ratio test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eThe models were adjusted for: sex; age; BMI; hypertension; diabetes mellitus; coronary heart disease; antihyperlipidemic; uric acid; fasting plasma glucose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003cstrong\u003es:\u003c/strong\u003e\u003cem\u003e\u0026nbsp;AIP\u003c/em\u003e atherogenic index of plasma.\u003c/p\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":"plasma atherogenic index, Rheumatoid arthritis, Disease activity level, Blood lipids, Nonlinear correlation","lastPublishedDoi":"10.21203/rs.3.rs-7722454/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7722454/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective: To explore the relationship between plasma atherogenic index (AIP) and disease activity of rheumatoid arthritis (RA).\u003c/p\u003e\n\u003cp\u003eMethod: This cross-sectional study included a total of 1088 RA patients. According to the third quartile of AIP, patients were divided into three groups (T1, T2, T3), and a multiple linear regression model was used to analyze the association between AIP and 28 joint disease range of motion scores (DAS28-ESR). In addition, a two-stage linear regression model is used to explore potential nonlinear relationships.\u003c/p\u003e\n\u003cp\u003eResult: After adjusting for confounding factors, multiple linear regression showed that, with T1 group as a reference, the DAS28-ESR scores of T2 group (β=0.159, 95% CI: 0.041~0.277) and T3 group (β=0.192, 95% CI: 0.071~0.314) were significantly higher (trend P-value=0.002). The analysis of AIP as a continuous variable also yielded consistent conclusions (β=0.312, 95% CI: 0.080~0.545). However, the two-segment linear regression revealed a significant nonlinear relationship (log likelihood ratio P＜0.001), with an inflection point of AIP=0.175. Before the inflection point (AIP\u0026lt;0.175), AIP was positively correlated with DAS28-ESR (β=0.927, 95% CI: 0.543-1.311); After the inflection point (AIP\u0026gt;0.175), AIP showed a negative correlation with DAS28-ESR (β=-1.010, 95% CI: -1.682~-0.339).\u003c/p\u003e\n\u003cp\u003eConclusion: There is a significant nonlinear correlation between AIP and RA disease activity. When AIP is lower than 0.175, it is positively correlated with disease activity; But when AIP exceeds this critical value, the correlation reverses. This result suggests that lipid metabolism may play a complex role in RA inflammation.\u003c/p\u003e","manuscriptTitle":"Non-linear Association between Atherogenic Index of Plasma and Disease Activity in Rheumatoid Arthritis: A Cross-sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-09 06:33:15","doi":"10.21203/rs.3.rs-7722454/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a54206b2-04ae-461b-83ef-81b2d1ed7b17","owner":[],"postedDate":"March 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64011065,"name":"Health sciences/Biomarkers"},{"id":64011066,"name":"Health sciences/Diseases"},{"id":64011068,"name":"Biological sciences/Immunology"},{"id":64011070,"name":"Health sciences/Medical research"},{"id":64011072,"name":"Health sciences/Rheumatology"},{"id":64011074,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-03-31T10:23:56+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-09 06:33:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7722454","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7722454","identity":"rs-7722454","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}