Association between total protein, albumin, globulin, albumin- globulin ratio and rheumatoid arthritis: evidence from NHANES and Mendelian randomization

Association between total protein, albumin, globulin, albumin- globulin ratio and rheumatoid arthritis: evidence from NHANES and Mendelian randomization | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between total protein, albumin, globulin, albumin- globulin ratio and rheumatoid arthritis: evidence from NHANES and Mendelian randomization Ke Liu, Le Zhang, Haoming Zhao, Zuyu Tang, Hua Sheng, Yixiao Xiong, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4251713/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background With the increasing incidence of rheumatoid arthritis (RA) and the increasing percentage of serum RF negativity, there is an urgent need for more and more accurate methods for the early diagnosis and prevention of RA, among which serum proteins are closely related to the development of RA and are expected to become new auxiliary diagnostic tools, but their relationship with RA is not clear, so this study aimed to investigate the causal relationship between total protein (TP), albumin (ALB), globulin ( GLB), and albumin-globulin ratio (A/G) on the causal relationship of rheumatoid arthritis (RA). Methods In this study, the relationship between TP, ALB, GLB, A/G and rheumatoid arthritis was comprehensively evaluated by generalized linear modeling and smoothed curve fitting through the data of serum proteins and RA in the NHANES(National Health and Nutrition Examination Survey) database; moreover, for the positive results with significant associations, the inverse variance weighted (IVW) method in Mendelian Randomization (MR) was used in conjunction with the other four methods to further validate and clarify the causative relationship, and finally, the results were subjected to the inspection of heterogeneity and horizontal polytomousness in order to assess whether the results were robust. Results In the observational study, after correction for confounders, TP, GLB, and A/G were not significantly associated with rheumatoid arthritis, whereas ALB was significantly negatively associated with rheumatoid arthritis (OR = 0.662, [95%CI = 0.507–0.864], P = 0.003), and subgroup analyses showed significant negative associations in both males and females (male : OR = 0.674, [95%CI = 0.458–0.991], P = 0.047; females: OR = 0.661, [95%CI = 0.437–0.999], P = 0.049). In further MR analysis, IVW: ALB on RA, OR = 0.70 [95%0.52–0.93], P = 0.013; RA on ALB, OR = 0.95 [95%CI = 0.93–0.98], P < 0.001.The results of the MR analyses remained consistent with NHANES. Conclusion There is a significant relationship between ALB and RA, and the reduction of ALB may be one of the risk factors for RA, as well as one of the outcomes in the development of RA. serum proteins albumin rheumatoid arthritis NHANES Mendelian randomization Figures Figure 1 Figure 2 Figure 3 Background RA is a chronic systemic immune disease that erodes symmetrically and causes inflammation of the joints, with pannus and synovitis as its main pathologic features, and morning stiffness, swelling and deformity of the joints, and bone destruction as its clinical manifestations[ 1 ]. Global epidemiology shows that since 1990 to 2017, the global prevalence of the disease has increased by 7.4%, the incidence of the disease has increased by 8.2%, and the number of people with disabilities has risen year by year[ 2 ]. Currently, the disease is mainly improved and slowed down by conventional antirheumatic drugs, biologics, and targeted drugs[ 3 ]. Although it is found that genetics, gender and various environmental factors may be related to the development of RA[ 4 ], there are still some pathological mechanisms have not been fully clarified. RA is often diagnosed by serum testing combined with clinical symptoms, but the incidence of serum RF-positive RA is gradually decreasing, while the incidence of serum RF-negative RA is significantly increasing[ 5 , 6 ]. Therefore, it is of great significance to explore the methods of early diagnosis and prevention of RA. Biomarkers can be used for the diagnosis of RA patients, assessment of disease changes and response to treatment[ 7 ], of which serum proteins are susceptible to disease and readily available, and have a role in the identification of RA[ 8 ]. Serum proteins are easily affected by the disease and are readily available. Serum proteins, including ALB, are the most important proteins in human plasma and play an important role in maintaining nutrition and osmolality. In addition, several studies have shown an association between ALB and RA disease activity[ 9 – 12 ]. It presents lower levels during the acute inflammatory response[ 13 ]. GLB has been associated with tooth loss in RA patients[ 14 ]. GLB is associated with human immunity and has a strong association with RA-induced vasculitis[ 15 ]. In addition, fibroblasts play a key role in immune regulation by producing various proteins in healthy and diseased states. They are involved in lymphocyte migration, complement activation, inflammation, acute phase response and immune regulation. These molecules have an important role in autoinflammatory diseases such as RA[ 16 ]. Although these small amounts of evidence above suggest that there is some association between certain serum proteins and RA, there is a lack of evidence supported by large data and large sample sizes[ 17 ]. so we conducted this study to obtain a higher level of evidence to determine their relationship. Methods Transect studies Study design and participants NHANES is a cross-sectional, population-based survey conducted by the Centers for Disease Control and Prevention (CDC) to assess the health and nutritional status of adults and children in the U.S. The NHANES survey provides a rich, realistic, and representative set of data for clinical research. health and nutrition data. We included NHANES data from 1999–2016, totaling 9 cycles, in our study. Participants with complete information on RA, TP, ALB, GLB and covariates after screening were included in the study. NHANES Serum Protein and RA Assessment For participants TP, ALB, and GLB were assessed by Standard Biochemistry Profile from Laboratory Data, and A/G was calculated based on the ratio of ALB to GLB.RA was assessed by Arthritis-related questions from Questionnaire. Participants were asked two questions related to RA: (1)Has a doctor or other health professional ever told {you/SP} that {you/s/he}. . .had arthritis?༈2༉Which type of arthritis was it? If the participant answered "yes" to the first question and "RA" to the second question, the participant was determined to be an individual with RA, otherwise a non-RA individual. Other covariates used in NHANES To control for potential confounders, we included the following covariates:Age[ 18 ], Gender[ 2 ], Ethnicity[ 2 ], Education[ 19 ], Income[ 2 ], Smoke[ 20 ], Alcohol[ 21 , 22 ], BMI[ 23 ], Hypertension[ 24 ], High Cholesterol[ 25 ], bloodglucose[ 26 ]. These confounders were chosen to consider possible associations with the prevalence of RA. NHANES analysis We first conducted an NHANES analysis using Three generalized linear models were designed to assess the relationship between TP, ALB, GLB, A/G, and RA. Model 1 was unadjusted (including gender); model 2 was adjusted for gender, age, race, education, and income; and model 3 added adjustments for smoking, alcohol consumption, body mass index, hypertension, hyperlipidemia, and serum glucose to model 2. Smoothed curve fitting was also used to observe the trend of change between the two and the presence or absence of a threshold, and generalized linear models were used before and after the threshold point to further analyze the trend and significance. Considering the gender differences in RA, the study analyses were subjected to gender subgroup analyses. In addition, NHANES uses complex multistage probability sampling, and this study corrected for multistage, stratification, over-sampling, and weighting in the generalized linear model and population description to ensure the accuracy of the results. Mendelian randomization Research design MR is a statistical method for estimating the causal effect of exposure factors on outcome variables. Similar to a randomized controlled trial and because genes follow the principle of random assignment, the results are not subject to the confounding factors and reverse causal associations found in traditional epidemiological studies.MR determines the relationship between exposure and outcome through the combination of the exposure and the outcome's SINGLE NUCLEOTIDE POLYMORPHISM (SNP). SNPs as working variables need to meet three major assumptions: (1) the assumption of association, where the SNP needs to be strongly associated with the exposure factor; (2) the assumption of exclusivity, where this SNP can only affect the outcome by influencing the exposure and not through other pathways or modalities; and (3) the assumption of independence, where this SNP can only affect the exposure and not directly the outcome. We obtained genome-wide association study (GWAS) summary statistics related to this study through the IEU OpenGWAS project ( https://gwas.mrcieu.ac.uk/ ). MR analysis Based on the results of NHANES analysis, we analyzed serum proteins with strong association with RA by MR to further verify the correlation and causality. To obtain SNP values strongly correlated with exposure, the p-value between SNP and exposure was set at a significant level < 5e-08, and to exclude SNPs with linkage disequilibrium, r 2 < 0.001 and clumping distance = 10000 kb were set. Instrumental variables that do not strongly correlate with exposure factors or can only explain a small portion of the phenotypic variance were removed, and finally all F-test values were required to be > 10, and finally we removed SNPs associated with confounding factors with the help of R. After that, MR analyses were performed. In this study, IVW, which has the strongest causal detection capability, was used as the main research method, however, the IVW method requires that genetic variation can only affect the results through exposure, so we used a more stringent approach, and all covariates of model 3 in the NHANES study were used as confounders in order to minimize the bias generated by confounders, and the removal of confounders was carried out with the help of the R package, and the confounders The keywords were set as Age, income, BMI, Body Mass Index, Obesity, fat, glucose, diabetes, Ethnicity, race, education, Smoke, Alcohol, Hypertension, blood pressure. cholesterol,triglyceride, and reverse MR set the confounding factor keywords to liver, cancer, and nutrition. The causality was also determined by combining the other 4 MR methods (MR Egger, Weighted median, Simple mode, Weighted mode), and if the results of the 5 methods were similar and the IVW method was significant, a causal relationship was considered. In addition, we used Cochran's Q test to assess heterogeneity, Egger intercept to assess horizontal pleiotropy, and MR-PRESSO to detect abnormal SNPs (outliers), and exclude outlier SNPs to obtain estimates closer to the true values. Finally, the "MR-PRESSO distortion test" was utilized to examine whether there is a difference between the pre-corrected and post-corrected results. leave-one-out was used to exclude each SNP one by one and the remaining SNPs were recalculated to see whether the results were significantly affected by a particular SNP. We plotted the results as a scatterplot, where each point corresponds to a SNP, showing the association between that genetic variant and Exposure and Outcome. Lines of different colors indicate fitting by different methods, showing the association between Exposure and Outcome predicted by all SNPs. Forest plots show the effect size of each SNP and its 95% confidence interval. Funnel plots are used to detect heterogeneity among genetic variants. If the funnel plot shows a symmetrical shape, this usually means that there is no significant heterogeneity, i.e., there is no systematic bias between the study effect and its accuracy. All exposures and outcomes were analyzed by bidirectional MR to clarify the presence of bidirectional causal effects. All NHANES statistical analyses were performed using EmpowerStats software.MR analyses were performed with the help of TwoSampleMR[ 27 ], MRPRESSO[ 28 ], LdlinkR[ 29 , 30 ], dplyr, grid, and Forestploter's Packages on the R 4.3.3 platform. Results Results of NHANES analysis Epidemiological Observational Analysis In the 1999–2016 study cycle, after screening the final 11,961 participants were included in the research study, of which there were 980 patients with RA, accounting for 8.19% of the total number of the study, and the inclusion and exclusion process is shown in Fig. 1 .The final inclusion population was analyzed epidemiologically under the consideration of the weights, of which 48.7% of the males and 51.3% of the females were analyzed in terms of the subgroups by gender of the Under the analysis of weights, most of the male RA patients were elderly, low-income, smoking, drinking, hypertension, hyperlipidemia, hyperglycemia, and there were differences between different education levels and races, and there were no significant differences in body weight and smoking history between RA and non-RA. The population distribution trends of female RA patients were generally similar to those of male patients, but there were also differences in weight and smoking history between female RA and non-RA. Therefore these could be potential influencing factors for RA, and the results are shown in Table 1 . Table 1 Demographic Characteristics, Associated Diseases, Weighted, of RA or Non-RA Participants from the 1999-2016 Cycle of NHANES. Characteristics Total Male Female Non-RA RA P-value Non-RA RA P-value Age (years) 50.4 (50.0 ,50.8) 50.2 (49.7, 50.8) 58.4 (56.6, 60.2) <0.0001 49.5 (49.0, 50.1) 56.8 (55.4, 58.1) <0.0001 Income 3.0 (3.0 ,3.1) 3.1 (3.1, 3.2) 2.7 (2.4, 2.9) <0.0001 3.0 (2.9,3.1) 2.3 (2.1, 2.5) <0.0001 BMI (kg/m2) 28.8 (28.7,29.0) 28.7 (28.6, 28.9) 29.3 (28.6, 30.1) 0.109 28.8 (28.5,29.0) 30.6 (29.6, 31.5) 0.0004 Blood glucose (mmol/L) 5.5 (5.5 ,5.6) 5.7 (5.6 ,5.7) 5.9 (5.7,6.0) 0.0392 5.4 (5.3, 5.4) 5.6 (5.5, 5.8) 0.0012 STP (g/dL) 7.1 (7.1 ,7.1) 7.2 (7.1, 7.2) 7.2 (7.1, 7.2) 0.947 7.1 (7.0 ,7.1) 7.0 (7.0 ,7.1) 0.2937 SAL (g/dL) 4.3 (4.3 ,4.3) 4.4 (4.4 ,4.4) 4.3 (4.2, 4.3) <0.0001 4.2 (4.2, 4.2) 4.1 (4.1, 4.2) <0.0001 SGB (g/dL) 2.8 (2.8 ,2.8) 2.8 (2.8 ,2.8) 2.9 (2.8,3.0) 0.0037 2.8 (2.8, 2.9) 2.9 (2.8,3.0) 0.11 AGR 1.6 (1.6 ,1.6) 1.6 (1.6 ,1.6) 1.5 (1.5 ,1.6) 0.0001 1.5 (1.5 ,1.5) 1.5 (1.4 ,1.5) 0.0278 Ethnicity (%) 0.0242 0.0001 Mexican American 5.2 (4.4, 6.1) 6.5 (5.5 ,7.7) 5.4 (3.9,7.5) 3.7 (3.0 ,4.5) 3.8 (2.6,5.5) Other Hispanic 4.3 (3.5, 5.2) 4.9 (4.0 ,5.9) 2.9 (1.5 ,5.5) 3.7 (2.9, 4.7) 4.7 (2.9, 7.6) Non-Hispanic White 77.4 (75.3, 79.3) 75.0 (72.7, 77.1) 76.4 (71.0, 81.0) 80.6 (78.6 ,82.5) 73.8 (68.9, 78.2) Non-Hispanic Black 8.1 (7.1, 9.2) 7.7 (6.8, 8.8) 11.1 (8.6, 14.3) 7.8 (6.7, 9.1) 14.1 (11.0 ,17.8) Other Race 5.1 (4.5, 5.7) 5.9 (5.2, 6.8) 4.2 (2.4, 7.4) 4.3 (3.6,5.0) 3.6 (1.9 ,6.6) Education (%) <0.0001 <0.0001 Less than 9th grade 4.5 (4.1,5.0) 5.2 (4.7, 5.8) 8.7 (6.3,12.0) 3.1 (2.5, 3.7) 7.5 (5.5,10.2) 9-11th grade 12.6 (11.6, 13.7) 12.6 (11.3, 13.9) 17.4 (13.7, 21.9) 11.9 (10.8, 13.1) 18.2 (14.6, 22.4) High school or equivalent 25.7 (24.5, 26.9) 26.3 (24.7, 27.9) 28.6 (23.5, 34.4) 24.4 (22.9, 25.8) 31.6 (25.8 ,38.0) Some college or AA degree 33.7 (32.3, 35.2) 31.1 (29.4, 32.9) 32.5 (26.9, 38.6) 37.2 (35.4, 39.0) 32.3 (26.8, 38.4) College graduate or above 23.4 (21.7, 25.2) 24.9 (23.0 ,26.9) 12.7 (8.8,18.0) 23.5 (21.3, 25.8) 10.3 (6.9, 15.2) Smoke (%) 0.1715 0.0402 Every day 33.8 (32.4, 35.3) 31.7 (30.1, 33.3) 31.5 (25.8, 37.7) 35.8 (33.7, 38.1) 43.5 (37.6, 49.6) Some days 7.4 (6.8,8.0) 8.1 (7.2, 9.1) 5.1 (3.2, 8.2) 6.8 (6.1, 7.6) 5.5 (3.3, 9.1) Not at all 58.8 (57.2, 60.3) 60.2 (58.4, 61.9) 63.4 (56.8, 69.5) 57.3 (55.1, 59.5) 51.0 (44.7, 57.2) Alcohol (%) 0.0001 <0.0001 Yes 86.6 (85.6, 87.7) 92.0 (91.1, 92.7) 85.3 (80.4, 89.2) 81.5 (79.7, 83.3) 70.7 (64.9, 75.9) No 13.4 (12.3, 14.4) 8.0 (7.3 ,8.9) 14.7 (10.8, 19.6) 18.5 (16.7, 20.3) 29.3 (24.1, 35.1) Hypertension (%) <0.0001 <0.0001 Yes 36.7 (35.5,38.0) 36.7 (35.0 ,38.5) 52.4 (45.9, 58.9) 34.3 (32.7, 35.9) 53.7 (47.4, 60.0) No 63.3 (62.0 ,64.5) 63.3 (61.5,65.0) 47.6 (41.1, 54.1) 65.7 (64.1, 67.3) 46.3 (40.0 ,52.6) High cholesterol (%) 0.0018 0.0182 Yes 40.9 (39.8, 42.1) 42.7 (41.2, 44.3) 54.3 (47.0 ,61.5) 37.4 (35.7, 39.2) 45.5 (38.6 ,52.5) No 59.1 (57.9, 60.2) 57.3 (55.7, 58.8) 45.7 (38.5 ,53.0) 62.6 (60.8, 64.3) 54.5 (47.5, 61.4) Data in the table. For continuous variables: survey-weighted mean (95% CI) , P-value was by survey-weighted linear regression (svyglm) For categorical variables: survey-weighted percentage (95% CI) , P-value was by survey-weighted Chi-square test (svytable) RA, rheumatoid arthritis; Incme, Ratio of family income to poverty; BMI, Body Mass Index; TP, total protein; ALB, albumin; GLB, globulin; A/G, albumin-to-globulin ratio Table 2 Association between different serum proteins and RA and gender differences, weighted Model 1 Model 2 Model 3 exp(coef) 95% CI low 95% CI upp P.value exp(coef) 95% CI low 95% CI upp P.value exp(coef) 95% CI low 95% CI upp P.value Total protein 0.893 0.723 1.103 0.297 0.913 0.749 1.113 0.369 0.945 0.773 1.155 0.583 Male 0.989 0.716 1.367 0.947 1.052 0.789 1.403 0.728 1.084 0.815 1.441 0.581 Female 0.854 0.637 1.146 0.295 0.789 0.594 1.047 0.103 0.828 0.621 1.104 0.201 Albumin 0.388 0.306 0.493 0.000 0.590 0.456 0.764 0.000 0.662 0.507 0.864 0.003 Male 0.358 0.249 0.515 0.000 0.639 0.434 0.942 0.025 0.674 0.458 0.991 0.047 Female 0.446 0.321 0.619 0.000 0.563 0.385 0.823 0.004 0.661 0.437 0.999 0.049 Globulin 1.500 1.227 1.833 0.000 1.166 0.938 1.450 0.168 1.130 0.905 1.412 0.282 Male 1.656 1.220 2.247 0.002 1.301 0.944 1.791 0.110 1.303 0.952 1.782 0.101 Female 1.315 0.952 1.817 0.098 1.020 0.731 1.423 0.907 0.969 0.685 1.370 0.858 AGR 0.400 0.275 0.583 0.000 0.668 0.452 0.987 0.045 0.743 0.502 1.100 0.140 Male 0.339 0.195 0.589 0.000 0.600 0.345 1.046 0.074 0.617 0.358 1.065 0.082 Female 0.519 0.282 0.954 0.037 0.768 0.420 1.405 0.394 0.900 0.490 1.651 0.734 Model 1 adiust for: None Model 2 adiust for: Gender,Age,Ethnicity,Education,Income Model 3 adiust for: Gender,Age,Ethnicity,Education,Income,Smoke,Alcohol,BMI,Hypertension,High Cholesterol,Diabete,ALT,AST,Total Calcium, Glucose,Triglycerides,Triglycerides,Creatinine Glucose,Triglycerides,Creatinine exp(coef), HR-hazard ratio; 95% CI, 95% confidence interval；AGR,, albumin-to-globulin ratio Table 3 Results of threshold analysis of the relationship between TP, GLB and RA in female subgroups exp(coef) 95% CI low 95% CI upp P.value GLB2.9 Segment effects 0.902 0.561 1.449 0.670 TP7.4 Segment effects 1.176 0.533 2.593 0.689 GLB, globulin; TP, total protein; exp(coef), HR-hazard ratio; 95% CI, 95% confidence interval Table 5 Heterogeneity and horizontal pleiotropy Traits F statistics MR-PRESSO Cochran's Q test (MR-Egger) Cochran's Q test (IVW) Horizontal pleiotropy tests RSSobs P value Q P value Q P value Intercept SE P value ALB on RA 49.656 37.356 0.715 27.818 0.723 27.890 0.761 -0.002 0.008 0.790 RA on ALB 68.469 48.133 <0.001 38.184 0.000 39.348 0.000 -0.002 0.004 0.574 ALB on RA, causality analysis of exposure to ALB on outcome RA; RA on ALB, causality analysis of exposure to RA on outcome ALB Relationship between TP, ALB, GLB, A/G and RA in NHANES The results of the generalized linear model showed that the relationship between TP and RA was not significantly correlated (P > 0.05).ALB, GLB and A/G were all associated with RA (P < 0.05).In model 2, after adjusting for confounders, the relationship between GLB and RA was no longer significant, and the relationship between A/G and RA was not significant in both male and female gender strata, and only the association between ALB and RA was still strong in all groups. groups remained strongly associated, and in Model 3, after further adjustment for confounders, ALB and RA retained this association, with no change in direction, and it was significant in both males and females. The results are shown in Table 2 Results of Smoothing Curve Fitting and Threshold Effect between TP, ALB, GLB, A/G and RA in NHANES We observed the nonlinear relationship between RA and TP, ALB, GLB, and A/G by smoothing curve fitting, while adding all adjustment variables in Model 3 for adjustment. The results showed that TP and GLB were positively correlated with RA, and ALB, A/G showed negative correlation with RA. In the gender subgroup analysis, the different genders still had the same trend in general. Detailed results are shown in Fig. 2 (a ~ h).The relationship between TP and RA had a threshold effect in women, with a fold point of 7.4 g/dL after threshold calculation, and the relationship between GLB and RA also had a threshold effect in women, with a fold point of 2.9 g/dL after calculation.The generalized linear model was again used for the calculation of the relevant effect values before and after the fold point, and the results showed that the relationship between both of them and RA before and after the fold point was still not significant. Specific results are shown in Table 3 . MR results Causal relationship between ALB and RA Based on the results of the NHNAES analysis, we further validated the association between ALB and RA by bidirectional MR using the Saori Sakaue[ 31 ] The RA dataset (GWAS ID, ebi-a-GCST90018910) summarized from the study of Saori Sakaue et al. which is a European population containing a sample size of 417,256.Mbatchou J[ 32 ] et al. summarized the serum ALB dataset (GWAS ID, ebi-a-GCST90013990), which contains a total sample size of 357968. Through rigorous screening of SNPs and removal of SNPs associated with confounders, 35 SNPs were ultimately used in the causal analysis of ALB on RA and 13 SNPs were used in the causal analysis of RA on ALB. The results of the five MR analyses were finally summarized in a table, see Table 4 , and the results of the IVW method study showed that there was a bidirectional causal relationship between serum ALB and RA, (IVW: ALB on RA, OR = 0.70 (0.52–0.93), P = 0.013); RA on ALB, OR = 0.95 (0.93–0.98), P < 0.001). Tests for Heterogeneity and Horizontal Multiple Validity Accordingly, we further tested the MR results of serum ALB and RA for heterogeneity and horizontal multiple validity, which showed that the causal relationship of ALB to RA was very robust without heterogeneity and horizontal validity, and the causal relationship of RA to ALB by the MR-PRESSO method suggested that there was heterogeneity, but the results were corrected to show that a significant negative correlation still existed. See Table 5 . plotted leave-one-out, SNP forest plot, scatterplot, and funnel plot of ALB versus RA. See Fig. 3 (ALB-RA: a, c, e, g; RA-RA: b, d, f, h) Discussion RA is one of the most common immune-mediated inflammatory diseases that, if not recognized and treated in time, can lead to severe joint damage and disability[ 33 ]. Despite the progress made in this disease, the diagnosis and treatment of early RA still face great challenges[ 34 ]. The pathogenesis of RA, especially in the autoantibody-negative subgroup, is still poorly understood[ 35 ]. In seronegative RA patients, the use of other biomarkers can prevent false negatives and aid in the accurate diagnosis of RA[ 36 ]. in which serum proteins are strongly associated with RA and fluctuate in response to changes in RA[ 37 ]. However, there are fewer studies between serum proteins and RA, lacking large sample sizes and reliable validation of results. This study provides new evidence for the relationship between TP, ALB, GLB, A/G and RA. Through statistical analysis of NHANES we found that there was no significant correlation between serum TP and RA. The relationship between GLB and A/G and RA was significant in model 1 (unadjusted model), however, with the addition of the adjustment variables, their relationship was no longer significant. This suggests that the relationship between ALB, GLB, A/G and RA is not robust and that their explanatory power for the incidence of RA is not sufficient, probably because the elderly, low-income, low-education, and non-Spanish populations are prone to the disease of RA, and that with the addition of these adjusting variables, the factors that are truly associated with RA produce a stronger determinant of the incidence of RA. ALB was the only one of these factors that had a significant association with RA and the results were robust. The analysis of the NHANES study showed a significant negative association between ALB and RA in the unadjusted model 1. And the same trend and significance of association existed across gender. This trend of significance and association remained after further addition of covariant variable, and finally in model 3 where all variables were added, the two subgroups showed that the association remained. In further MR analysis, the IVW method in bidirectional MR showed a negative and significant association between ALB and RA in both directions. The direction of the association remained consistent in the remaining four methods. there was no significant heterogeneity or horizontal pleiotropy detection in the causal relationship of ALB to RA, and the results of the leave-one-out method remained relatively robust regardless of which SNP result was excluded, and the overall range of variation of the error line was small. Meanwhile, the SNP forest plot showed that most of the SNP results were negatively correlated. The scatterplot showed that all MR methods had consistent directionality and the Egger intercept was almost zero, and the funnel plot was roughly symmetrical, so ALB reduction was a risk factor for RA and the results were very robust.In the causal analysis of RA on ALB, the IVW method suggested that it was also negatively correlated, and the results of the remaining four methods were similar. Cochran's Q test, funnel plot and MR-PRESSO method suggested the presence of heterogeneity, but the corrected results showed that there was still a significant negative correlation, which indicated that although there was heterogeneity among the selected instrumental variables, it did not affect the results of IVW. Therefore the results remain robust. Accordingly, we believe that the results of NHANES and MR analyses doubly demonstrate the existence of a negative correlation between ALB and RA. The bidirectional causality between RA and ALB may be due to the fact that on the one hand, nutritional deficiencies and decreased immunity caused by hypoproteinaemia may contribute to the onset of RA, and on the other hand, RA can likewise cause a decrease in ALB, thus appearing to be a bidirectional causality.The negative correlation between ALB and RA may be caused by these mechanisms.Firstly, with the gradual progression of RA, about 24.7% of patients with RA develop malnutrition with hypoALBemia[ 38 ], The lowering of ALB may reflect the deterioration associated with RA[ 39 ]. This characteristic progression of malnutrition in RA patients is associated with advanced age, increased inflammatory disease activity, and decreased quality of life[ 40 ]. Wasting atrophy attributable to excessive proteolytic metabolism and useless atrophy caused by inflammatory cytokines[ 38 ], namely tumor necrosis factor alpha (TNFα), interleukin 1 (IL-1) and interferon gamma, which are mediators of RA inflammation[ 41 ], that regulate fat, carbohydrate and protein metabolism[ 42 ]. Hypermetabolism driven by these cytokines can cause malnutrition in hypoALBemia, and, in addition, dysfunction leads to wasting muscle atrophy, which further accelerates protein catabolism. Second, ALB exchange between plasma and synovial fluid exists in RA and is in dynamic equilibrium[ 43 ]. Synovial permeability is increased in patients with RA, e.g., 6-fold ALB permeability and approximately 40-fold increase in giant GLB in the knee[ 44 ]. Labeling of ALB by radioactive technetium-99m labeling revealed that RA disease synovitis showed traces of ALB in arthrography[ 45 ], demonstrating the transfer of serum ALB into the joints. In addition, ALB clearance in rheumatoid knee arthritis effusions was significantly higher than in knee osteoarthritis effusions[ 46 ]. Thus there is a significant metabolic depletion of ALB in the joints, which causes intra-articular transfer of serum ALB, further exacerbating the depletion and reduction of ALB. Third, as RA shifts from stable to active phase, urinary ALB excretion increases in RA patients[ 47 ] and urinary ALB excretion was significantly correlated with CRP and disease activity in RA patients[ 48 ] . The mechanism of negative association of ALB in RA, i.e., metabolic depletion in synovial fluid and increased synovial permeability, and the transport of serum ALB into the joints, thus making ALB inflammatory-targeting ability, and the ability of ALB to actively target disease sites in RA, along with leukocyte's excellent cytocompatibility, degradability in biological tissues, non-antigenicity, and safety profile, implies that ALB can be serve as drug carriers for RA therapy[ 49 ]. For example, Andreas Wunder[ 50 ] et al. used an arthritic mouse model to study the pharmacokinetics and efficacy of covalent coupling of methotrexate to ALB and found that the amount of ALB accumulated in inflamed paws was significantly higher, whereas the liver and kidneys contained significantly lower amounts of ALB, which provides additional ideas for the treatment of RA. Although we double-validated our results, there are some limitations; in NHANES, the pathogenesis and influencing factors of RA patients have not been fully clarified, and there is the possibility of residual confounding, while serum proteins are affected by a variety of factors, and there may be inaccuracy of measurement. In addition, in MR, different sex gender and age may have different causal effects, thus failing to stratify the analysis, moreover, there exists developmental compensation (canalization), for certain adverse exposures, individuals may develop compensatory mechanisms during long-term development to reduce the impact of adverse genetic factors, which may cause an overestimation of the effect value. Finally, the population investigated by NHANES was a U.S. population and, limited to data sources, a European population was selected for MR; the consistency of the results for these two populations is an issue that needs to be further explored. Conclusion In conclusion, we believe that there is a negative association between ALB and RA, and the reduction of ALB may be one of the risk factors for RA, but it may also be one of the results during the development of RA.ALB can be used as a biomarker for the auxiliary diagnosis of RA.In addition, for the patients with diagnosed RA, an appropriate high-protein diet can be used as a supplement for the depletion of ALB, meanwhile ALB is expected to be a new drug carrier for RA. Abbreviations RA=Rheumatoid Arthritis TP=Total Protein ALB=Albumin GLB=Globulin A/G=albumin-globulin ratio SNP=single nucleotide polymorphism IVW=Inverse variance weighted NHANES=National Health and Nutrition Examination Survey MR=Mendelian randomization Declarations Ethical approval and consent to participate This study used publicly available databases, the NHANES study was approved by the Research Ethics Review Board of the National Center for Health Statistics, the included GWAS data were approved by the appropriate ethics committees, and all participants signed a written informed consent form. Ethical review and approval was not required for this study. Consent for publication Not applicable. Availability of data and materials All raw data are publicly available at NHANES (https://www.cdc.gov/nchs/nhanes/index.htm) and leu open gwas project (https://gwas.mrcieu.ac.uk/). Competing interests No conflict of interest Funding No fund support Author's contribution All authors contributed to the design and conceptualization of this study, LK, ZL, ZHM pair carried out the preparation of the material, data cleaning, LK, TZY, HS, XYX completed the data analysis, LK, ZL completed the writing of the manuscript, OL, KJJ critically revised the manuscript. All authors read and approved agreed to the final version of the manuscript. Acknowledgements We thank all the volunteers and participants who participated in the National Health and Nutrition Examination Survey and Whole Gene Association Sequencing, and the designers of the R language and R package. We thank them for making this study possible with their free dedication. References Smith MH, Berman JR. What Is Rheumatoid Arthritis? JAMA 2022;327: 1194. Finckh A, Gilbert B, Hodkinson B, Bae SC, Thomas R, Deane KD, et al. Global epidemiology of rheumatoid arthritis. Nat Rev Rheumatol 2022;18: 591-602. Fraenkel L, Bathon JM, England BR, St CE, Arayssi T, Carandang K, et al. 2021 American College of Rheumatology Guideline for the Treatment of Rheumatoid Arthritis. Arthritis Care Res (Hoboken) 2021;73: 924-39. Deane KD, Demoruelle MK, Kelmenson LB, Kuhn KA, Norris JM, Holers VM. Genetic and environmental risk factors for rheumatoid arthritis. Best Pract Res Clin Rheumatol 2017;31: 3-18. Myasoedova E, Davis J, Matteson EL, Crowson CS. Is the epidemiology of rheumatoid arthritis changing? Results from a population-based incidence study, 1985-2014. Ann Rheum Dis 2020;79: 440-4. Bugatti S, De Stefano L, Gandolfo S, Ciccia F, Montecucco C. Autoantibody-negative rheumatoid arthritis: still a challenge for the rheumatologist. Lancet Rheumatol 2023;5: e743-55. Atzeni F, Talotta R, Masala IF, Bongiovanni S, Boccassini L, Sarzi-Puttini P. Biomarkers in Rheumatoid Arthritis. Isr Med Assoc J 2017;19: 512-6. Mc AA, Kwasnik A, Szentpetery A, Hernandez B, Parnell A, de Jager W, et al. Identification and Evaluation of Serum Protein Biomarkers That Differentiate Psoriatic Arthritis From Rheumatoid Arthritis. Arthritis Rheumatol 2022;74: 81-91. Kaplan H, Cengiz G, Sas S, Eldemir YO. Is the C-reactive protein-to-albumin ratio the most remarkable simple inflammatory marker showing active disease in patients with axial spondyloarthritis, psoriatic arthritis, and rheumatoid arthritis? Clin Rheumatol 2023;42: 2959-69. He Y, Tang J, Wu B, Yang B, Ou Q, Lin J. Correlation between albumin to fibrinogen ratio, C-reactive protein to albumin ratio and Th17 cells in patients with rheumatoid arthritis. Clin Chim Acta 2020;500: 149-54. Yang WM, Zhang WH, Ying HQ, Xu YM, Zhang J, Min QH, et al. Two new inflammatory markers associated with disease activity score-28 in patients with rheumatoid arthritis: Albumin to fibrinogen ratio and C-reactive protein to albumin ratio. Int Immunopharmacol 2018;62: 293-8. Chen S, Ying H, Du J, Zhu X, Shi J, Zhang Y, et al. The association between albumin-dNLR score and disease activity in patients with rheumatoid arthritis. J Clin Lab Anal 2019;33: e22695. Eckart A, Struja T, Kutz A, Baumgartner A, Baumgartner T, Zurfluh S, et al. Relationship of Nutritional Status, Inflammation, and Serum Albumin Levels During Acute Illness: A Prospective Study. Am J Med 2020;133: 713-22. Mochizuki T, Hoshi K, Yano K, Koyama J, Kukidome H, Ikari K, et al. Smoking, Serum Albumin and 25-hydroxy Vitamin D Levels, and Bone Mineral Density Are Associated with Tooth Loss in Patients with Rheumatoid Arthritis. Intern Med 2023;62: 2821-5. Allen C, Elson CJ, Scott DG, Bacon PA, Bucknall RC. IgG antiglobulins in rheumatoid arthritis and other arthritides: relationship with clinical features and other parameters. Ann Rheum Dis 1981;40: 127-31. Jafari N, Gheitasi R, Khorasani HR, Golpour M, Mehri M, Nayeri K, et al. Proteome analysis, bioinformatic prediction and experimental evidence revealed immune response down-regulation function for serum-starved human fibroblasts. Heliyon 2023;9: e19238. LUSH B, CROWLEY MF, FLETCHER E, BUCHAN JF. Total and differential protein levels in the blood and cerebrospinal fluid in rheumatoid arthritis. Ann Rheum Dis 1951;10: 153-62. Luo WD, Wang YP, Lv J, Liu Y, Qu YQ, Xu XF, et al. Age-related self-DNA accumulation may accelerate arthritis in rats and in human rheumatoid arthritis. Nat Commun 2023;14: 4394. Zhao SS, Holmes MV, Zheng J, Sanderson E, Carter AR. The impact of education inequality on rheumatoid arthritis risk is mediated by smoking and body mass index: Mendelian randomization study. Rheumatology (Oxford) 2022;61: 2167-75. Gianfrancesco MA, Crowson CS. Where There's Smoke, There's a Joint: Passive Smoking and Rheumatoid Arthritis. Arthritis Rheumatol 2021;73: 2161-2. Azizov V, Zaiss MM. Alcohol Consumption in Rheumatoid Arthritis: A Path through the Immune System. Nutrients 2021;13. Hubner M, Zaiss MM, Azizov V. Double-edged sword: Alcohol's effect on rheumatoid arthritis and beyond. Joint Bone Spine 2024;91: 105626. Karlsson T, Hadizadeh F, Rask-Andersen M, Johansson A, Ek WE. Body Mass Index and the Risk of Rheumatic Disease: Linear and Nonlinear Mendelian Randomization Analyses. Arthritis Rheumatol 2023;75: 2027-35. Hadwen B, Stranges S, Barra L. Risk factors for hypertension in rheumatoid arthritis patients-A systematic review. Autoimmun Rev 2021;20: 102786. VanEvery H, Yang W, Su J, Olsen N, Bao L, Lu B, et al. Low-Density Lipoprotein Cholesterol and the Risk of Rheumatoid Arthritis: A Prospective Study in a Chinese Cohort. Nutrients 2022;14. Masuko K. Glucose as a Potential Key to Fuel Inflammation in Rheumatoid Arthritis. Nutrients 2022;14. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife 2018;7. Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 2018;50: 693-8. Lin SH, Brown DW, Machiela MJ. LDtrait: An Online Tool for Identifying Published Phenotype Associations in Linkage Disequilibrium. Cancer Res 2020;80: 3443-6. Myers TA, Chanock SJ, Machiela MJ. LDlinkR: An R Package for Rapidly Calculating Linkage Disequilibrium Statistics in Diverse Populations. Front Genet 2020;11: 157. Sakaue S, Kanai M, Tanigawa Y, Karjalainen J, Kurki M, Koshiba S, et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet 2021;53: 1415-24. Mbatchou J, Barnard L, Backman J, Marcketta A, Kosmicki JA, Ziyatdinov A, et al. Computationally efficient whole-genome regression for quantitative and binary traits. Nat Genet 2021;53: 1097-103. Brown P, Pratt AG, Hyrich KL. Therapeutic advances in rheumatoid arthritis. BMJ 2024;384: e070856. Cush JJ. Rheumatoid Arthritis: Early Diagnosis and Treatment. Rheum Dis Clin North Am 2022;48: 537-47. Bugatti S, De Stefano L, Gandolfo S, Ciccia F, Montecucco C. Autoantibody-negative rheumatoid arthritis: still a challenge for the rheumatologist. Lancet Rheumatol 2023;5: e743-55. Mun S, Lee J, Park M, Shin J, Lim MK, Kang HG. Serum biomarker panel for the diagnosis of rheumatoid arthritis. Arthritis Res Ther 2021;23: 31. WALLIS AD. The serum proteins in rheumatoid arthritis. Ann Intern Med 1950;32: 63-71. Fukuda W, Yamazaki T, Akaogi T, Hayashi H, Kusakabe T, Tsubouchi Y, et al. Malnutrition and disease progression in patients with rheumatoid arthritis. Mod Rheumatol 2005;15: 104-7. Hayashi H, Satoi K, Sato-Mito N, Kaburagi T, Yoshino H, Higaki M, et al. Nutritional status in relation to adipokines and oxidative stress is associated with disease activity in patients with rheumatoid arthritis. Nutrition 2012;28: 1109-14. Cano-Garcia L, Redondo-Rodriguez R, Manrique-Arija S, Dominguez-Quesada C, Crisostomo VJ, Armenteros-Ortiz P, et al. Prevalence of Malnutrition and Associated Factors in Older Patients with Rheumatoid Arthritis: A Cross-Sectional Study. Nutrients 2023;15. Tani K, Shimizu T, Motoki Y, Sone S. Chemokines in synovial inflammation in rheumatoid arthritis: basic and clinical aspects. Mod Rheumatol 2002;12: 93-9. Johnson RW. Inhibition of growth by pro-inflammatory cytokines: an integrated view. J Anim Sci 1997;75: 1244-55. Brown DL, Cooper AG, Bluestone R. Exchange of IgM and albumin between plasma and synovial fluid in rheumatoid arthritis. Ann Rheum Dis 1969;28: 644-51. Levick JR. Permeability of rheumatoid and normal human synovium to specific plasma proteins. Arthritis Rheum 1981;24: 1550-60. Liberatore M, Clemente M, Iurilli AP, Zorzin L, Marini M, Di Rocco E, et al. Scintigraphic evaluation of disease activity in rheumatoid arthritis: a comparison of technetium-99m human non-specific immunoglobulins, leucocytes and albumin nanocolloids. Eur J Nucl Med 1992;19: 853-7. Wallis WJ, Simkin PA, Nelp WB, Foster DM. Intraarticular volume and clearance in human synovial effusions. Arthritis Rheum 1985;28: 441-9. Pieringer H, Brummaier T, Piringer B, Auer-Hackenberg L, Hartl A, Puchner R, et al. Urinary Albumin Excretion and Vascular Function in Rheumatoid Arthritis. J Korean Med Sci 2016;31: 382-8. Pedersen LM, Nordin H, Svensson B, Bliddal H. Microalbuminuria in patients with rheumatoid arthritis. Ann Rheum Dis 1995;54: 189-92. Garg U, Jain N, Kaul S, Nagaich U. Role of Albumin as a Targeted Drug Carrier in the Management of Rheumatoid Arthritis: A Comprehensive Review. Mol Pharm 2023;20: 5345-58. Wunder A, Muller-Ladner U, Stelzer EH, Funk J, Neumann E, Stehle G, et al. Albumin-based drug delivery as novel therapeutic approach for rheumatoid arthritis. J Immunol 2003;170: 4793-801. 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4251713","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":290331224,"identity":"23a1c829-09b9-4e7e-915a-4b1b639d1c92","order_by":0,"name":"Ke Liu","email":"","orcid":"","institution":"Hunan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Liu","suffix":""},{"id":290331225,"identity":"1c224d90-4eb7-45d1-9b3a-81a786c3acfb","order_by":1,"name":"Le Zhang","email":"","orcid":"","institution":"Hunan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Le","middleName":"","lastName":"Zhang","suffix":""},{"id":290331226,"identity":"11789cd6-b7e8-4b9d-ac40-5d274d1950f8","order_by":2,"name":"Haoming Zhao","email":"","orcid":"","institution":"Hunan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Haoming","middleName":"","lastName":"Zhao","suffix":""},{"id":290331229,"identity":"eacb1ba1-ee42-473a-8394-5e6537c26cb4","order_by":3,"name":"Zuyu Tang","email":"","orcid":"","institution":"Hunan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zuyu","middleName":"","lastName":"Tang","suffix":""},{"id":290331231,"identity":"52e344c9-b684-4ca1-a2d3-7ef3a401d10a","order_by":4,"name":"Hua Sheng","email":"","orcid":"","institution":"Hunan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hua","middleName":"","lastName":"Sheng","suffix":""},{"id":290331234,"identity":"ae86734f-c0de-4d13-b91e-3ae102b9dfd2","order_by":5,"name":"Yixiao Xiong","email":"","orcid":"","institution":"Hunan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yixiao","middleName":"","lastName":"Xiong","suffix":""},{"id":290331237,"identity":"24fdc77a-f7e1-478a-bd79-094b6f2bc26d","order_by":6,"name":"Liang Ou","email":"","orcid":"","institution":"Hunan Academy of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Ou","suffix":""},{"id":290331241,"identity":"70679d21-420b-4294-9430-4c9e4c2c67e7","order_by":7,"name":"Jianjun Kuang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYBACNv7+jw8+GNjU88sfPvggoaKGsBY+iQPGhjMK0hIkZ7AlGzw4c4ywFjmGBDNpjg+HEgxm8KhJPmxhJsJhDAeSjRkMDuQZSPewVSQ2sDHwt3cn4NfC3HDwcYHBnWJzmbPHbiTukGGQOHN2AwFbDjYbzzB4xrizIS/tRuIZNgYDiVxCWpLZpHkMDjNuOJBjVpDYxkyMljSwlsQNN3LMGIjTInGG2XCGQZqxZM+xZImEM8d4CPpFvr+H8cGHPzZy/OzNBz/+qKiR42/vxa8FA/CQpnwUjIJRMApGAVYAACCoThlL1sn0AAAAAElFTkSuQmCC","orcid":"","institution":"Hunan Academy of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Jianjun","middleName":"","lastName":"Kuang","suffix":""}],"badges":[],"createdAt":"2024-04-11 10:05:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4251713/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4251713/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54743593,"identity":"60c19155-773b-4477-92eb-03bd05888a6e","added_by":"auto","created_at":"2024-04-16 06:41:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":30200,"visible":true,"origin":"","legend":"\u003cp\u003eNHANES inclusion, exclusion process\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4251713/v1/242d7b61b1336025a95f9a38.png"},{"id":54743595,"identity":"5c313136-a446-45fc-85af-3cbd0449f064","added_by":"auto","created_at":"2024-04-16 06:41:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":368334,"visible":true,"origin":"","legend":"\u003cp\u003eResults of smoothing curve fitting between TP, ALB, GLB, A/G and RA\u003c/p\u003e\n\u003cp\u003eTP, total protein; ALB, albumin; GLB, globulin; A/G, albumin-to-globulin ratio;\u003c/p\u003e\n\u003cp\u003eRA, rheumatoid arthritis\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4251713/v1/610b0190563fd617e0463d8c.png"},{"id":54743594,"identity":"8a9f1a2f-0f29-4c8d-9a01-24a402bdccba","added_by":"auto","created_at":"2024-04-16 06:41:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":407260,"visible":true,"origin":"","legend":"\u003cp\u003eHeterogeneity and horizontal pleiotropy\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4251713/v1/e18b8e53c12d42959ed974b2.png"},{"id":55264947,"identity":"8115b49a-940e-4f3f-bb9e-a864deb74c1f","added_by":"auto","created_at":"2024-04-25 01:51:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1168797,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4251713/v1/4c6c0e2c-73d6-4f25-9527-0c1d5e86f78e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between total protein, albumin, globulin, albumin- globulin ratio and rheumatoid arthritis: evidence from NHANES and Mendelian randomization","fulltext":[{"header":"Background","content":"\u003cp\u003eRA is a chronic systemic immune disease that erodes symmetrically and causes inflammation of the joints, with pannus and synovitis as its main pathologic features, and morning stiffness, swelling and deformity of the joints, and bone destruction as its clinical manifestations[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Global epidemiology shows that since 1990 to 2017, the global prevalence of the disease has increased by 7.4%, the incidence of the disease has increased by 8.2%, and the number of people with disabilities has risen year by year[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Currently, the disease is mainly improved and slowed down by conventional antirheumatic drugs, biologics, and targeted drugs[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although it is found that genetics, gender and various environmental factors may be related to the development of RA[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], there are still some pathological mechanisms have not been fully clarified. RA is often diagnosed by serum testing combined with clinical symptoms, but the incidence of serum RF-positive RA is gradually decreasing, while the incidence of serum RF-negative RA is significantly increasing[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, it is of great significance to explore the methods of early diagnosis and prevention of RA.\u003c/p\u003e \u003cp\u003eBiomarkers can be used for the diagnosis of RA patients, assessment of disease changes and response to treatment[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], of which serum proteins are susceptible to disease and readily available, and have a role in the identification of RA[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Serum proteins are easily affected by the disease and are readily available. Serum proteins, including ALB, are the most important proteins in human plasma and play an important role in maintaining nutrition and osmolality. In addition, several studies have shown an association between ALB and RA disease activity[\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. It presents lower levels during the acute inflammatory response[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. GLB has been associated with tooth loss in RA patients[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. GLB is associated with human immunity and has a strong association with RA-induced vasculitis[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In addition, fibroblasts play a key role in immune regulation by producing various proteins in healthy and diseased states. They are involved in lymphocyte migration, complement activation, inflammation, acute phase response and immune regulation. These molecules have an important role in autoinflammatory diseases such as RA[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough these small amounts of evidence above suggest that there is some association between certain serum proteins and RA, there is a lack of evidence supported by large data and large sample sizes[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. so we conducted this study to obtain a higher level of evidence to determine their relationship.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTransect studies\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eNHANES is a cross-sectional, population-based survey conducted by the Centers for Disease Control and Prevention (CDC) to assess the health and nutritional status of adults and children in the U.S. The NHANES survey provides a rich, realistic, and representative set of data for clinical research. health and nutrition data. We included NHANES data from 1999\u0026ndash;2016, totaling 9 cycles, in our study. Participants with complete information on RA, TP, ALB, GLB and covariates after screening were included in the study.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eNHANES Serum Protein and RA Assessment\u003c/h2\u003e \u003cp\u003eFor participants TP, ALB, and GLB were assessed by Standard Biochemistry Profile from Laboratory Data, and A/G was calculated based on the ratio of ALB to GLB.RA was assessed by Arthritis-related questions from Questionnaire. Participants were asked two questions related to RA: (1)Has a doctor or other health professional ever told {you/SP} that {you/s/he}. . .had arthritis?༈2༉Which type of arthritis was it? If the participant answered \"yes\" to the first question and \"RA\" to the second question, the participant was determined to be an individual with RA, otherwise a non-RA individual.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOther covariates used in NHANES\u003c/h2\u003e \u003cp\u003eTo control for potential confounders, we included the following covariates:Age[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], Gender[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], Ethnicity[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], Education[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], Income[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], Smoke[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], Alcohol[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], BMI[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], Hypertension[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], High Cholesterol[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], bloodglucose[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These confounders were chosen to consider possible associations with the prevalence of RA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eNHANES analysis\u003c/h2\u003e \u003cp\u003eWe first conducted an NHANES analysis using Three generalized linear models were designed to assess the relationship between TP, ALB, GLB, A/G, and RA. Model 1 was unadjusted (including gender); model 2 was adjusted for gender, age, race, education, and income; and model 3 added adjustments for smoking, alcohol consumption, body mass index, hypertension, hyperlipidemia, and serum glucose to model 2. Smoothed curve fitting was also used to observe the trend of change between the two and the presence or absence of a threshold, and generalized linear models were used before and after the threshold point to further analyze the trend and significance. Considering the gender differences in RA, the study analyses were subjected to gender subgroup analyses. In addition, NHANES uses complex multistage probability sampling, and this study corrected for multistage, stratification, over-sampling, and weighting in the generalized linear model and population description to ensure the accuracy of the results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMendelian randomization\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eResearch design\u003c/h2\u003e \u003cp\u003eMR is a statistical method for estimating the causal effect of exposure factors on outcome variables. Similar to a randomized controlled trial and because genes follow the principle of random assignment, the results are not subject to the confounding factors and reverse causal associations found in traditional epidemiological studies.MR determines the relationship between exposure and outcome through the combination of the exposure and the outcome's SINGLE NUCLEOTIDE POLYMORPHISM (SNP). SNPs as working variables need to meet three major assumptions: (1) the assumption of association, where the SNP needs to be strongly associated with the exposure factor; (2) the assumption of exclusivity, where this SNP can only affect the outcome by influencing the exposure and not through other pathways or modalities; and (3) the assumption of independence, where this SNP can only affect the exposure and not directly the outcome. We obtained genome-wide association study (GWAS) summary statistics related to this study through the IEU OpenGWAS project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMR analysis\u003c/h2\u003e \u003cp\u003eBased on the results of NHANES analysis, we analyzed serum proteins with strong association with RA by MR to further verify the correlation and causality. To obtain SNP values strongly correlated with exposure, the p-value between SNP and exposure was set at a significant level\u0026thinsp;\u0026lt;\u0026thinsp;5e-08, and to exclude SNPs with linkage disequilibrium, r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and clumping distance\u0026thinsp;=\u0026thinsp;10000 kb were set. Instrumental variables that do not strongly correlate with exposure factors or can only explain a small portion of the phenotypic variance were removed, and finally all F-test values were required to be \u0026gt;\u0026thinsp;10, and finally we removed SNPs associated with confounding factors with the help of R. After that, MR analyses were performed.\u003c/p\u003e \u003cp\u003eIn this study, IVW, which has the strongest causal detection capability, was used as the main research method, however, the IVW method requires that genetic variation can only affect the results through exposure, so we used a more stringent approach, and all covariates of model 3 in the NHANES study were used as confounders in order to minimize the bias generated by confounders, and the removal of confounders was carried out with the help of the R package, and the confounders The keywords were set as Age, income, BMI, Body Mass Index, Obesity, fat, glucose, diabetes, Ethnicity, race, education, Smoke, Alcohol, Hypertension, blood pressure. cholesterol,triglyceride, and reverse MR set the confounding factor keywords to liver, cancer, and nutrition. The causality was also determined by combining the other 4 MR methods (MR Egger, Weighted median, Simple mode, Weighted mode), and if the results of the 5 methods were similar and the IVW method was significant, a causal relationship was considered. In addition, we used Cochran's Q test to assess heterogeneity, Egger intercept to assess horizontal pleiotropy, and MR-PRESSO to detect abnormal SNPs (outliers), and exclude outlier SNPs to obtain estimates closer to the true values. Finally, the \"MR-PRESSO distortion test\" was utilized to examine whether there is a difference between the pre-corrected and post-corrected results. leave-one-out was used to exclude each SNP one by one and the remaining SNPs were recalculated to see whether the results were significantly affected by a particular SNP. We plotted the results as a scatterplot, where each point corresponds to a SNP, showing the association between that genetic variant and Exposure and Outcome. Lines of different colors indicate fitting by different methods, showing the association between Exposure and Outcome predicted by all SNPs. Forest plots show the effect size of each SNP and its 95% confidence interval. Funnel plots are used to detect heterogeneity among genetic variants. If the funnel plot shows a symmetrical shape, this usually means that there is no significant heterogeneity, i.e., there is no systematic bias between the study effect and its accuracy. All exposures and outcomes were analyzed by bidirectional MR to clarify the presence of bidirectional causal effects.\u003c/p\u003e \u003cp\u003eAll NHANES statistical analyses were performed using EmpowerStats software.MR analyses were performed with the help of TwoSampleMR[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], MRPRESSO[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], LdlinkR[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], dplyr, grid, and Forestploter's Packages on the R 4.3.3 platform.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eResults of NHANES analysis\u003c/h2\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003eEpidemiological Observational Analysis\u003c/h2\u003e\n \u003cp\u003eIn the 1999\u0026ndash;2016 study cycle, after screening the final 11,961 participants were included in the research study, of which there were 980 patients with RA, accounting for 8.19% of the total number of the study, and the inclusion and exclusion process is shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.The final inclusion population was analyzed epidemiologically under the consideration of the weights, of which 48.7% of the males and 51.3% of the females were analyzed in terms of the subgroups by gender of the Under the analysis of weights, most of the male RA patients were elderly, low-income, smoking, drinking, hypertension, hyperlipidemia, hyperglycemia, and there were differences between different education levels and races, and there were no significant differences in body weight and smoking history between RA and non-RA. The population distribution trends of female RA patients were generally similar to those of male patients, but there were also differences in weight and smoking history between female RA and non-RA. Therefore these could be potential influencing factors for RA, and the results are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eTable 1 Demographic Characteristics, Associated Diseases, Weighted, of RA or Non-RA Participants from the 1999-2016 Cycle of NHANES.\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"931\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\" rowspan=\"2\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\" rowspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.25671321160043%\" colspan=\"3\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"34.371643394199786%\" colspan=\"3\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.936507936507937%\"\u003e\n \u003cp\u003eNon-RA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.936507936507937%\"\u003e\n \u003cp\u003eRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.476190476190476%\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.015873015873016%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.365079365079364%\"\u003e\n \u003cp\u003eNon-RA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.365079365079364%\"\u003e\n \u003cp\u003eRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\n \u003cp\u003e50.4 (50.0 ,50.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e50.2 (49.7, 50.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e58.4 (56.6, 60.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e49.5 (49.0, 50.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e56.8 (55.4, 58.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\n \u003cp\u003e3.0 (3.0 ,3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e3.1 (3.1, 3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e2.7 (2.4, 2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e3.0 (2.9,3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e2.3 (2.1, 2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eBMI (kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\n \u003cp\u003e28.8 (28.7,29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e28.7 (28.6, 28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e29.3 (28.6, 30.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e28.8 (28.5,29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e30.6 (29.6, 31.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eBlood glucose (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\n \u003cp\u003e5.5 (5.5 ,5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e5.7 (5.6 ,5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e5.9 (5.7,6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\n \u003cp\u003e0.0392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e5.4 (5.3, 5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e5.6 (5.5, 5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\n \u003cp\u003e0.0012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eSTP (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\n \u003cp\u003e7.1 (7.1 ,7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e7.2 (7.1, 7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e7.2 (7.1, 7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e7.1 (7.0 ,7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e7.0 (7.0 ,7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\n \u003cp\u003e0.2937\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eSAL (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\n \u003cp\u003e4.3 (4.3 ,4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e4.4 (4.4 ,4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e4.3 (4.2, 4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e4.2 (4.2, 4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e4.1 (4.1, 4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eSGB (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\n \u003cp\u003e2.8 (2.8 ,2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e2.8 (2.8 ,2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e2.9 (2.8,3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\n \u003cp\u003e0.0037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e2.8 (2.8, 2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e2.9 (2.8,3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eAGR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\n \u003cp\u003e1.6 (1.6 ,1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e1.6 (1.6 ,1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e1.5 (1.5 ,1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e1.5 (1.5 ,1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e1.5 (1.4 ,1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\n \u003cp\u003e0.0278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eEthnicity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\n \u003cp\u003e0.0242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\n \u003cp\u003e5.2 (4.4, 6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e6.5 (5.5 ,7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e5.4 (3.9,7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e3.7 (3.0 ,4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e3.8 (2.6,5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\n \u003cp\u003e4.3 (3.5, 5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e4.9 (4.0 ,5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e2.9 (1.5 ,5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e3.7 (2.9, 4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e4.7 (2.9, 7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\n \u003cp\u003e77.4 (75.3, 79.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e75.0 (72.7, 77.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e76.4 (71.0, 81.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e80.6 (78.6 ,82.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e73.8 (68.9, 78.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\n \u003cp\u003e8.1 (7.1, 9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e7.7 (6.8, 8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e11.1 (8.6, 14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e7.8 (6.7, 9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e14.1 (11.0 ,17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eOther Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\n \u003cp\u003e5.1 (4.5, 5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e5.9 (5.2, 6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e4.2 (2.4, 7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e4.3 (3.6,5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e3.6 (1.9 ,6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eEducation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eLess than 9th grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\n \u003cp\u003e4.5 (4.1,5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e5.2 (4.7, 5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e8.7 (6.3,12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e3.1 (2.5, 3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e7.5 (5.5,10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003e9-11th grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\n \u003cp\u003e12.6 (11.6, 13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e12.6 (11.3, 13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e17.4 (13.7, 21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e11.9 (10.8, 13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e18.2 (14.6, 22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eHigh school or equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\n \u003cp\u003e25.7 (24.5, 26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e26.3 (24.7, 27.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e28.6 (23.5, 34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e24.4 (22.9, 25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e31.6 (25.8 ,38.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eSome college or AA degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\n \u003cp\u003e33.7 (32.3, 35.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e31.1 (29.4, 32.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e32.5 (26.9, 38.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e37.2 (35.4, 39.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e32.3 (26.8, 38.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eCollege graduate or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\n \u003cp\u003e23.4 (21.7, 25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e24.9 (23.0 ,26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e12.7 (8.8,18.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e23.5 (21.3, 25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e10.3 (6.9, 15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eSmoke (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\n \u003cp\u003e0.1715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\n \u003cp\u003e0.0402\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eEvery day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\n \u003cp\u003e33.8 (32.4, 35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e31.7 (30.1, 33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e31.5 (25.8, 37.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e35.8 (33.7, 38.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e43.5 (37.6, 49.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eSome days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\n \u003cp\u003e7.4 (6.8,8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e8.1 (7.2, 9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e5.1 (3.2, 8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e6.8 (6.1, 7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e5.5 (3.3, 9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eNot at all\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\n \u003cp\u003e58.8 (57.2, 60.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e60.2 (58.4, 61.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e63.4 (56.8, 69.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e57.3 (55.1, 59.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e51.0 (44.7, 57.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eAlcohol (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\n \u003cp\u003e86.6 (85.6, 87.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e92.0 (91.1, 92.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e85.3 (80.4, 89.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e81.5 (79.7, 83.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e70.7 (64.9, 75.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\n \u003cp\u003e13.4 (12.3, 14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e8.0 (7.3 ,8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e14.7 (10.8, 19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e18.5 (16.7, 20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e29.3 (24.1, 35.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eHypertension (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\n \u003cp\u003e36.7 (35.5,38.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e36.7 (35.0 ,38.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e52.4 (45.9, 58.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e34.3 (32.7, 35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e53.7 (47.4, 60.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\n \u003cp\u003e63.3 (62.0 ,64.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e63.3 (61.5,65.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e47.6 (41.1, 54.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e65.7 (64.1, 67.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e46.3 (40.0 ,52.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eHigh cholesterol (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\n \u003cp\u003e0.0018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\n \u003cp\u003e0.0182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\n \u003cp\u003e40.9 (39.8, 42.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e42.7 (41.2, 44.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e54.3 (47.0 ,61.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e37.4 (35.7, 39.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e45.5 (38.6 ,52.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.548872180451127%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.781954887218046%\"\u003e\n \u003cp\u003e59.1 (57.9, 60.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e57.3 (55.7, 58.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1374865735768%\"\u003e\n \u003cp\u003e45.7 (38.5 ,53.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.089151450053706%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.0408163265306123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e62.6 (60.8, 64.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.104189044038668%\"\u003e\n \u003cp\u003e54.5 (47.5, 61.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.055853920515574%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eData in the table.\u003c/p\u003e\n \u003cp\u003eFor continuous variables: survey-weighted mean (95% CI) , P-value was by survey-weighted linear regression (svyglm)\u003c/p\u003e\n \u003cp\u003eFor categorical variables: survey-weighted percentage (95% CI) , P-value was by survey-weighted Chi-square test (svytable)\u003c/p\u003e\n \u003cp\u003eRA, rheumatoid arthritis; Incme, Ratio of family income to poverty; BMI, Body Mass Index; TP, total protein; ALB, albumin; GLB, globulin; A/G, albumin-to-globulin ratio\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTable 2 Association between different serum proteins and RA and gender differences, weighted\u003c/p\u003e\n \u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"1107\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.930442637759711%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.461607949412826%\" colspan=\"6\" valign=\"bottom\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.6260162601626016%\" valign=\"bottom\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.539295392953928%\" colspan=\"8\" valign=\"bottom\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.7163504968383017%\" valign=\"bottom\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.726287262872628%\" colspan=\"7\" valign=\"bottom\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.566787003610107%\" valign=\"bottom\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eexp(coef)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.212996389891696%\" valign=\"bottom\"\u003e\n \u003cp\u003e95% CI low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e95% CI upp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.324909747292419%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eP.value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.6245487364620939%\" valign=\"bottom\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" colspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003eexp(coef)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"bottom\"\u003e\n \u003cp\u003e95% CI low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e95% CI upp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.046931407942238%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eP.value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.7148014440433212%\" valign=\"bottom\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" valign=\"bottom\"\u003e\n \u003cp\u003eexp(coef)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"bottom\"\u003e\n \u003cp\u003e95% CI low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e95% CI upp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.324909747292419%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eP.value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.09025270758122744%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.566787003610107%\"\u003e\n \u003cp\u003eTotal protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.893\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.212996389891696%\" valign=\"top\"\u003e\n \u003cp\u003e0.723\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.103\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.324909747292419%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.297\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.6245487364620939%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.913\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.749\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.113\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.046931407942238%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.369\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.7148014440433212%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" valign=\"top\"\u003e\n \u003cp\u003e0.945\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.773\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.155\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.324909747292419%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.583\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.09025270758122744%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.566787003610107%\" valign=\"bottom\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.989\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.212996389891696%\" valign=\"top\"\u003e\n \u003cp\u003e0.716\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.367\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.324909747292419%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.947\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.6245487364620939%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e1.052\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.789\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.403\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.046931407942238%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.728\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.7148014440433212%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" valign=\"top\"\u003e\n \u003cp\u003e1.084\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.815\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.441\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.324909747292419%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.581\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.09025270758122744%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.566787003610107%\" valign=\"bottom\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.854\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.212996389891696%\" valign=\"top\"\u003e\n \u003cp\u003e0.637\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.146\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.324909747292419%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.295\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.6245487364620939%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.789\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.594\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.047\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.046931407942238%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.103\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.7148014440433212%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" valign=\"top\"\u003e\n \u003cp\u003e0.828\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.621\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.104\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.324909747292419%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.201\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.09025270758122744%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.566787003610107%\" valign=\"bottom\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.388\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.212996389891696%\" valign=\"top\"\u003e\n \u003cp\u003e0.306\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.493\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.949458483754513%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.590\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.456\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.764\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.046931407942238%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.7148014440433212%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" valign=\"top\"\u003e\n \u003cp\u003e0.662\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.507\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.864\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.324909747292419%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.09025270758122744%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.566787003610107%\" valign=\"bottom\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.358\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.212996389891696%\" valign=\"top\"\u003e\n \u003cp\u003e0.249\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.515\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.949458483754513%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.639\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.434\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.942\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.046931407942238%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.025\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.7148014440433212%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" valign=\"top\"\u003e\n \u003cp\u003e0.674\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.458\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.991\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.324909747292419%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.09025270758122744%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.566787003610107%\" valign=\"bottom\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.446\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.212996389891696%\" valign=\"top\"\u003e\n \u003cp\u003e0.321\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.619\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.324909747292419%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.6245487364620939%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.563\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.385\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.823\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.046931407942238%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.7148014440433212%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" valign=\"top\"\u003e\n \u003cp\u003e0.661\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.437\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.999\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.324909747292419%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.049\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.09025270758122744%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.566787003610107%\" valign=\"bottom\"\u003e\n \u003cp\u003eGlobulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.500\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.212996389891696%\" valign=\"top\"\u003e\n \u003cp\u003e1.227\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.833\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.324909747292419%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.6245487364620939%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e1.166\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.938\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.450\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.046931407942238%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.168\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.7148014440433212%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" valign=\"top\"\u003e\n \u003cp\u003e1.130\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.905\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.412\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.324909747292419%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.282\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.09025270758122744%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.566787003610107%\" valign=\"bottom\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.656\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.212996389891696%\" valign=\"top\"\u003e\n \u003cp\u003e1.220\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.247\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.324909747292419%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.6245487364620939%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e1.301\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.944\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.791\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.046931407942238%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.110\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.7148014440433212%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" valign=\"top\"\u003e\n \u003cp\u003e1.303\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.952\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.782\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.324909747292419%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.101\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.09025270758122744%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.566787003610107%\" valign=\"bottom\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.315\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.212996389891696%\" valign=\"top\"\u003e\n \u003cp\u003e0.952\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.817\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.324909747292419%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.098\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.6245487364620939%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e1.020\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.731\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.423\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.046931407942238%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.907\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.7148014440433212%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" valign=\"top\"\u003e\n \u003cp\u003e0.969\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n 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\u003c/td\u003e\n \u003ctd width=\"5.324909747292419%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.6245487364620939%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.668\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.452\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.987\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.046931407942238%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.045\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.7148014440433212%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" valign=\"top\"\u003e\n \u003cp\u003e0.743\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.502\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.100\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.324909747292419%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.140\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.09025270758122744%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.566787003610107%\" valign=\"bottom\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.339\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.212996389891696%\" valign=\"top\"\u003e\n \u003cp\u003e0.195\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.589\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.324909747292419%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.6245487364620939%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.600\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.345\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.046\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.046931407942238%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.074\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.7148014440433212%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" valign=\"top\"\u003e\n \u003cp\u003e0.617\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.358\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.065\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.324909747292419%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.082\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.09025270758122744%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.566787003610107%\" valign=\"bottom\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.519\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.212996389891696%\" valign=\"top\"\u003e\n \u003cp\u003e0.282\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.954\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.324909747292419%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.037\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.6245487364620939%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.768\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.420\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.405\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.046931407942238%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.394\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.7148014440433212%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.498194945848375%\" valign=\"top\"\u003e\n \u003cp\u003e0.900\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.490\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.664259927797834%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.651\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.324909747292419%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.734\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.09025270758122744%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.220614828209765%\" colspan=\"5\" valign=\"bottom\"\u003e\n \u003cp\u003eModel 1 adiust for: None\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.047016274864376%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.685352622061483%\" colspan=\"3\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.4466546112115732%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.9837251356238697%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.115732368896927%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.047016274864376%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.8933092224231465%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.7179023508137432%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.509945750452079%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.211573236889693%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.1374321880651%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.9837251356238697%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.53658536585366%\" colspan=\"14\" valign=\"bottom\"\u003e\n \u003cp\u003eModel 2 adiust for: Gender,Age,Ethnicity,Education,Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.039747064137307%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.890695573622403%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.7163504968383017%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.504065040650406%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.20144534778681%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.130081300813009%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.9810298102981028%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"25\" valign=\"bottom\"\u003e\n \u003cp\u003eModel 3 adiust for: Gender,Age,Ethnicity,Education,Income,Smoke,Alcohol,BMI,Hypertension,High Cholesterol,Diabete,ALT,AST,Total Calcium, Glucose,Triglycerides,Triglycerides,Creatinine Glucose,Triglycerides,Creatinine\u003c/p\u003e\n \u003cp\u003eexp(coef), HR-hazard ratio; 95% CI, 95% confidence interval；AGR,, albumin-to-globulin ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTable 3 Results of threshold analysis of the relationship between TP, GLB and RA in female subgroups\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"517\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.81624758220503%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.44100580270793%\" valign=\"bottom\"\u003e\n \u003cp\u003eexp(coef)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.988394584139265%\" valign=\"bottom\"\u003e\n \u003cp\u003e95% CI low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.568665377176014%\" valign=\"bottom\"\u003e\n \u003cp\u003e95% CI upp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.18568665377176%\" valign=\"bottom\"\u003e\n \u003cp\u003eP.value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.81624758220503%\" valign=\"bottom\"\u003e\n \u003cp\u003eGLB\u0026lt;2.9 Segment effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.44100580270793%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.988394584139265%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.568665377176014%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.18568665377176%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.81624758220503%\" valign=\"bottom\"\u003e\n \u003cp\u003eGLB\u0026gt;2.9 Segment effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.44100580270793%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.988394584139265%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.568665377176014%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.18568665377176%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.81624758220503%\" valign=\"bottom\"\u003e\n \u003cp\u003eTP\u0026lt;7.4 Segment effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.44100580270793%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.988394584139265%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.568665377176014%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.18568665377176%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.81624758220503%\" valign=\"bottom\"\u003e\n \u003cp\u003eTP\u0026gt;7.4 Segment effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.44100580270793%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.988394584139265%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.568665377176014%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.18568665377176%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eGLB, globulin; TP, total protein; exp(coef), HR-hazard ratio; 95% CI, 95% confidence interval\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cp\u003eTable 5 Heterogeneity and horizontal pleiotropy\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"690\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.063953488372093%\" rowspan=\"2\"\u003e\n \u003cp\u003eTraits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.13953488372093%\" rowspan=\"2\"\u003e\n \u003cp\u003eF statistics\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.569767441860463%\" colspan=\"2\"\u003e\n \u003cp\u003eMR-PRESSO\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.761627906976744%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.116279069767442%\" colspan=\"2\"\u003e\n \u003cp\u003eCochran\u0026apos;s Q test\u0026nbsp;\u003cbr\u003e\u0026nbsp;(MR-Egger)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.9069767441860463%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.424418604651162%\" colspan=\"2\"\u003e\n \u003cp\u003eCochran\u0026apos;s Q test\u0026nbsp;\u003cbr\u003e\u0026nbsp;(IVW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.4709302325581395%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.546511627906977%\" colspan=\"3\"\u003e\n \u003cp\u003eHorizontal pleiotropy tests\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.382513661202186%\"\u003e\n \u003cp\u003eRSSobs\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.382513661202186%\"\u003e\n \u003cp\u003eP value\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.4608378870673953%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.561020036429872%\"\u003e\n \u003cp\u003eQ\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.382513661202186%\"\u003e\n \u003cp\u003eP value\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.6429872495446265%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.200364298724955%\"\u003e\n \u003cp\u003eQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.382513661202186%\"\u003e\n \u003cp\u003eP value\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.096539162112933%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.564663023679417%\"\u003e\n \u003cp\u003eIntercept\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.561020036429872%\"\u003e\n \u003cp\u003eSE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.382513661202186%\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.063953488372093%\"\u003e\n \u003cp\u003eALB on RA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.13953488372093%\"\u003e\n \u003cp\u003e49.656\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.284883720930232%\"\u003e\n \u003cp\u003e37.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.284883720930232%\"\u003e\n \u003cp\u003e0.715\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.761627906976744%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.8313953488372094%\"\u003e\n \u003cp\u003e27.818\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.284883720930232%\"\u003e\n \u003cp\u003e0.723\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.9069767441860463%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.13953488372093%\"\u003e\n \u003cp\u003e27.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.284883720930232%\"\u003e\n \u003cp\u003e0.761\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.4709302325581395%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.430232558139535%\"\u003e\n \u003cp\u003e-0.002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.8313953488372094%\"\u003e\n \u003cp\u003e0.008\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.284883720930232%\"\u003e\n \u003cp\u003e0.790\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.063953488372093%\"\u003e\n \u003cp\u003eRA on ALB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.13953488372093%\"\u003e\n \u003cp\u003e68.469\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.284883720930232%\"\u003e\n \u003cp\u003e48.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.284883720930232%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.761627906976744%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.8313953488372094%\"\u003e\n \u003cp\u003e38.184\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.284883720930232%\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.9069767441860463%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.13953488372093%\"\u003e\n \u003cp\u003e39.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.284883720930232%\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.4709302325581395%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.430232558139535%\"\u003e\n \u003cp\u003e-0.002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.8313953488372094%\"\u003e\n \u003cp\u003e0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.284883720930232%\"\u003e\n \u003cp\u003e0.574\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eALB on RA, causality analysis of exposure to ALB on outcome RA; RA on ALB, causality analysis of exposure to RA on outcome ALB\u003c/p\u003e\n \u003c/div\u003eRelationship between TP, ALB, GLB, A/G and RA in NHANES\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003cp\u003eThe results of the generalized linear model showed that the relationship between TP and RA was not significantly correlated (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).ALB, GLB and A/G were all associated with RA (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).In model 2, after adjusting for confounders, the relationship between GLB and RA was no longer significant, and the relationship between A/G and RA was not significant in both male and female gender strata, and only the association between ALB and RA was still strong in all groups. groups remained strongly associated, and in Model 3, after further adjustment for confounders, ALB and RA retained this association, with no change in direction, and it was significant in both males and females. The results are shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eResults of Smoothing Curve Fitting and Threshold Effect between TP, ALB, GLB, A/G and RA in NHANES\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWe observed the nonlinear relationship between RA and TP, ALB, GLB, and A/G by smoothing curve fitting, while adding all adjustment variables in Model 3 for adjustment. The results showed that TP and GLB were positively correlated with RA, and ALB, A/G showed negative correlation with RA. In the gender subgroup analysis, the different genders still had the same trend in general. Detailed results are shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e (a\u0026thinsp;~\u0026thinsp;h).The relationship between TP and RA had a threshold effect in women, with a fold point of 7.4 g/dL after threshold calculation, and the relationship between GLB and RA also had a threshold effect in women, with a fold point of 2.9 g/dL after calculation.The generalized linear model was again used for the calculation of the relevant effect values before and after the fold point, and the results showed that the relationship between both of them and RA before and after the fold point was still not significant. Specific results are shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eMR results\u003c/h2\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003eCausal relationship between ALB and RA\u003c/h2\u003e\n \u003cp\u003eBased on the results of the NHNAES analysis, we further validated the association between ALB and RA by bidirectional MR using the Saori Sakaue[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e] The RA dataset (GWAS ID, ebi-a-GCST90018910) summarized from the study of Saori Sakaue et al. which is a European population containing a sample size of 417,256.Mbatchou J[\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e] et al. summarized the serum ALB dataset (GWAS ID, ebi-a-GCST90013990), which contains a total sample size of 357968. Through rigorous screening of SNPs and removal of SNPs associated with confounders, 35 SNPs were ultimately used in the causal analysis of ALB on RA and 13 SNPs were used in the causal analysis of RA on ALB. The results of the five MR analyses were finally summarized in a table, see Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, and the results of the IVW method study showed that there was a bidirectional causal relationship between serum ALB and RA, (IVW: ALB on RA, OR\u0026thinsp;=\u0026thinsp;0.70 (0.52\u0026ndash;0.93), P\u0026thinsp;=\u0026thinsp;0.013); RA on ALB, OR\u0026thinsp;=\u0026thinsp;0.95 (0.93\u0026ndash;0.98), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eTests for Heterogeneity and Horizontal Multiple Validity\u003c/h2\u003e\n \u003cp\u003eAccordingly, we further tested the MR results of serum ALB and RA for heterogeneity and horizontal multiple validity, which showed that the causal relationship of ALB to RA was very robust without heterogeneity and horizontal validity, and the causal relationship of RA to ALB by the MR-PRESSO method suggested that there was heterogeneity, but the results were corrected to show that a significant negative correlation still existed. See Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. plotted leave-one-out, SNP forest plot, scatterplot, and funnel plot of ALB versus RA. See Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e (ALB-RA: a, c, e, g; RA-RA: b, d, f, h)\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eRA is one of the most common immune-mediated inflammatory diseases that, if not recognized and treated in time, can lead to severe joint damage and disability[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Despite the progress made in this disease, the diagnosis and treatment of early RA still face great challenges[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The pathogenesis of RA, especially in the autoantibody-negative subgroup, is still poorly understood[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In seronegative RA patients, the use of other biomarkers can prevent false negatives and aid in the accurate diagnosis of RA[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. in which serum proteins are strongly associated with RA and fluctuate in response to changes in RA[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, there are fewer studies between serum proteins and RA, lacking large sample sizes and reliable validation of results.\u003c/p\u003e \u003cp\u003eThis study provides new evidence for the relationship between TP, ALB, GLB, A/G and RA. Through statistical analysis of NHANES we found that there was no significant correlation between serum TP and RA. The relationship between GLB and A/G and RA was significant in model 1 (unadjusted model), however, with the addition of the adjustment variables, their relationship was no longer significant. This suggests that the relationship between ALB, GLB, A/G and RA is not robust and that their explanatory power for the incidence of RA is not sufficient, probably because the elderly, low-income, low-education, and non-Spanish populations are prone to the disease of RA, and that with the addition of these adjusting variables, the factors that are truly associated with RA produce a stronger determinant of the incidence of RA.\u003c/p\u003e \u003cp\u003eALB was the only one of these factors that had a significant association with RA and the results were robust. The analysis of the NHANES study showed a significant negative association between ALB and RA in the unadjusted model 1. And the same trend and significance of association existed across gender. This trend of significance and association remained after further addition of covariant variable, and finally in model 3 where all variables were added, the two subgroups showed that the association remained. In further MR analysis, the IVW method in bidirectional MR showed a negative and significant association between ALB and RA in both directions. The direction of the association remained consistent in the remaining four methods. there was no significant heterogeneity or horizontal pleiotropy detection in the causal relationship of ALB to RA, and the results of the leave-one-out method remained relatively robust regardless of which SNP result was excluded, and the overall range of variation of the error line was small. Meanwhile, the SNP forest plot showed that most of the SNP results were negatively correlated. The scatterplot showed that all MR methods had consistent directionality and the Egger intercept was almost zero, and the funnel plot was roughly symmetrical, so ALB reduction was a risk factor for RA and the results were very robust.In the causal analysis of RA on ALB, the IVW method suggested that it was also negatively correlated, and the results of the remaining four methods were similar. Cochran's Q test, funnel plot and MR-PRESSO method suggested the presence of heterogeneity, but the corrected results showed that there was still a significant negative correlation, which indicated that although there was heterogeneity among the selected instrumental variables, it did not affect the results of IVW. Therefore the results remain robust. Accordingly, we believe that the results of NHANES and MR analyses doubly demonstrate the existence of a negative correlation between ALB and RA.\u003c/p\u003e \u003cp\u003eThe bidirectional causality between RA and ALB may be due to the fact that on the one hand, nutritional deficiencies and decreased immunity caused by hypoproteinaemia may contribute to the onset of RA, and on the other hand, RA can likewise cause a decrease in ALB, thus appearing to be a bidirectional causality.The negative correlation between ALB and RA may be caused by these mechanisms.Firstly, with the gradual progression of RA, about 24.7% of patients with RA develop malnutrition with hypoALBemia[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], The lowering of ALB may reflect the deterioration associated with RA[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This characteristic progression of malnutrition in RA patients is associated with advanced age, increased inflammatory disease activity, and decreased quality of life[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Wasting atrophy attributable to excessive proteolytic metabolism and useless atrophy caused by inflammatory cytokines[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], namely tumor necrosis factor alpha (TNFα), interleukin 1 (IL-1) and interferon gamma, which are mediators of RA inflammation[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], that regulate fat, carbohydrate and protein metabolism[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Hypermetabolism driven by these cytokines can cause malnutrition in hypoALBemia, and, in addition, dysfunction leads to wasting muscle atrophy, which further accelerates protein catabolism. Second, ALB exchange between plasma and synovial fluid exists in RA and is in dynamic equilibrium[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Synovial permeability is increased in patients with RA, e.g., 6-fold ALB permeability and approximately 40-fold increase in giant GLB in the knee[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Labeling of ALB by radioactive technetium-99m labeling revealed that RA disease synovitis showed traces of ALB in arthrography[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], demonstrating the transfer of serum ALB into the joints. In addition, ALB clearance in rheumatoid knee arthritis effusions was significantly higher than in knee osteoarthritis effusions[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Thus there is a significant metabolic depletion of ALB in the joints, which causes intra-articular transfer of serum ALB, further exacerbating the depletion and reduction of ALB. Third, as RA shifts from stable to active phase, urinary ALB excretion increases in RA patients[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] and urinary ALB excretion was significantly correlated with CRP and disease activity in RA patients[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] .\u003c/p\u003e \u003cp\u003eThe mechanism of negative association of ALB in RA, i.e., metabolic depletion in synovial fluid and increased synovial permeability, and the transport of serum ALB into the joints, thus making ALB inflammatory-targeting ability, and the ability of ALB to actively target disease sites in RA, along with leukocyte's excellent cytocompatibility, degradability in biological tissues, non-antigenicity, and safety profile, implies that ALB can be serve as drug carriers for RA therapy[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. For example, Andreas Wunder[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] et al. used an arthritic mouse model to study the pharmacokinetics and efficacy of covalent coupling of methotrexate to ALB and found that the amount of ALB accumulated in inflamed paws was significantly higher, whereas the liver and kidneys contained significantly lower amounts of ALB, which provides additional ideas for the treatment of RA.\u003c/p\u003e \u003cp\u003eAlthough we double-validated our results, there are some limitations; in NHANES, the pathogenesis and influencing factors of RA patients have not been fully clarified, and there is the possibility of residual confounding, while serum proteins are affected by a variety of factors, and there may be inaccuracy of measurement. In addition, in MR, different sex gender and age may have different causal effects, thus failing to stratify the analysis, moreover, there exists developmental compensation (canalization), for certain adverse exposures, individuals may develop compensatory mechanisms during long-term development to reduce the impact of adverse genetic factors, which may cause an overestimation of the effect value. Finally, the population investigated by NHANES was a U.S. population and, limited to data sources, a European population was selected for MR; the consistency of the results for these two populations is an issue that needs to be further explored.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, we believe that there is a negative association between ALB and RA, and the reduction of ALB may be one of the risk factors for RA, but it may also be one of the results during the development of RA.ALB can be used as a biomarker for the auxiliary diagnosis of RA.In addition, for the patients with diagnosed RA, an appropriate high-protein diet can be used as a supplement for the depletion of ALB, meanwhile ALB is expected to be a new drug carrier for RA.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eRA=Rheumatoid Arthritis\u003c/p\u003e\n\u003cp\u003eTP=Total Protein\u003c/p\u003e\n\u003cp\u003eALB=Albumin\u003c/p\u003e\n\u003cp\u003eGLB=Globulin\u003c/p\u003e\n\u003cp\u003eA/G=albumin-globulin ratio\u003c/p\u003e\n\u003cp\u003eSNP=single nucleotide polymorphism\u003c/p\u003e\n\u003cp\u003eIVW=Inverse variance weighted\u003c/p\u003e\n\u003cp\u003eNHANES=National Health and Nutrition Examination Survey\u003c/p\u003e\n\u003cp\u003eMR=Mendelian randomization\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used publicly available databases, the NHANES study was approved by the Research Ethics Review Board of the National Center for Health Statistics, the included GWAS data were approved by the appropriate ethics committees, and all participants signed a written informed consent form. Ethical review and approval was not required for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll raw data are publicly available at NHANES (https://www.cdc.gov/nchs/nhanes/index.htm) and leu open gwas project (https://gwas.mrcieu.ac.uk/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflict of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo fund support\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026apos;s contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the design and conceptualization of this study, LK, ZL, ZHM pair carried out the preparation of the material, data cleaning, LK, TZY, HS, XYX completed the data analysis, LK, ZL completed the writing of the manuscript, OL, KJJ critically revised the manuscript. All authors read and approved agreed to the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the volunteers and participants who participated in the National Health and Nutrition Examination Survey and Whole Gene Association Sequencing, and the designers of the R language and R package. We thank them for making this study possible with their free dedication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSmith MH, Berman JR. What Is Rheumatoid Arthritis? JAMA 2022;327: 1194.\u003c/li\u003e\n\u003cli\u003eFinckh A, Gilbert B, Hodkinson B, Bae SC, Thomas R, Deane KD, et al. Global epidemiology of rheumatoid arthritis. Nat Rev Rheumatol 2022;18: 591-602.\u003c/li\u003e\n\u003cli\u003eFraenkel L, Bathon JM, England BR, St CE, Arayssi T, Carandang K, et al. 2021 American College of Rheumatology Guideline for the Treatment of Rheumatoid Arthritis. Arthritis Care Res (Hoboken) 2021;73: 924-39.\u003c/li\u003e\n\u003cli\u003eDeane KD, Demoruelle MK, Kelmenson LB, Kuhn KA, Norris JM, Holers VM. Genetic and environmental risk factors for rheumatoid arthritis. Best Pract Res Clin Rheumatol 2017;31: 3-18.\u003c/li\u003e\n\u003cli\u003eMyasoedova E, Davis J, Matteson EL, Crowson CS. Is the epidemiology of rheumatoid arthritis changing? Results from a population-based incidence study, 1985-2014. Ann Rheum Dis 2020;79: 440-4.\u003c/li\u003e\n\u003cli\u003eBugatti S, De Stefano L, Gandolfo S, Ciccia F, Montecucco C. Autoantibody-negative rheumatoid arthritis: still a challenge for the rheumatologist. Lancet Rheumatol 2023;5: e743-55.\u003c/li\u003e\n\u003cli\u003eAtzeni F, Talotta R, Masala IF, Bongiovanni S, Boccassini L, Sarzi-Puttini P. Biomarkers in Rheumatoid Arthritis. Isr Med Assoc J 2017;19: 512-6.\u003c/li\u003e\n\u003cli\u003eMc AA, Kwasnik A, Szentpetery A, Hernandez B, Parnell A, de Jager W, et al. Identification and Evaluation of Serum Protein Biomarkers That Differentiate Psoriatic Arthritis From Rheumatoid Arthritis. Arthritis Rheumatol 2022;74: 81-91.\u003c/li\u003e\n\u003cli\u003eKaplan H, Cengiz G, Sas S, Eldemir YO. Is the C-reactive protein-to-albumin ratio the most remarkable simple inflammatory marker showing active disease in patients with axial spondyloarthritis, psoriatic arthritis, and rheumatoid arthritis? Clin Rheumatol 2023;42: 2959-69.\u003c/li\u003e\n\u003cli\u003eHe Y, Tang J, Wu B, Yang B, Ou Q, Lin J. Correlation between albumin to fibrinogen ratio, C-reactive protein to albumin ratio and Th17 cells in patients with rheumatoid arthritis. Clin Chim Acta 2020;500: 149-54.\u003c/li\u003e\n\u003cli\u003eYang WM, Zhang WH, Ying HQ, Xu YM, Zhang J, Min QH, et al. Two new inflammatory markers associated with disease activity score-28 in patients with rheumatoid arthritis: Albumin to fibrinogen ratio and C-reactive protein to albumin ratio. Int Immunopharmacol 2018;62: 293-8.\u003c/li\u003e\n\u003cli\u003eChen S, Ying H, Du J, Zhu X, Shi J, Zhang Y, et al. The association between albumin-dNLR score and disease activity in patients with rheumatoid arthritis. J Clin Lab Anal 2019;33: e22695.\u003c/li\u003e\n\u003cli\u003eEckart A, Struja T, Kutz A, Baumgartner A, Baumgartner T, Zurfluh S, et al. Relationship of Nutritional Status, Inflammation, and Serum Albumin Levels During Acute Illness: A Prospective Study. Am J Med 2020;133: 713-22.\u003c/li\u003e\n\u003cli\u003eMochizuki T, Hoshi K, Yano K, Koyama J, Kukidome H, Ikari K, et al. Smoking, Serum Albumin and 25-hydroxy Vitamin D Levels, and Bone Mineral Density Are Associated with Tooth Loss in Patients with Rheumatoid Arthritis. Intern Med 2023;62: 2821-5.\u003c/li\u003e\n\u003cli\u003eAllen C, Elson CJ, Scott DG, Bacon PA, Bucknall RC. IgG antiglobulins in rheumatoid arthritis and other arthritides: relationship with clinical features and other parameters. Ann Rheum Dis 1981;40: 127-31.\u003c/li\u003e\n\u003cli\u003eJafari N, Gheitasi R, Khorasani HR, Golpour M, Mehri M, Nayeri K, et al. Proteome analysis, bioinformatic prediction and experimental evidence revealed immune response down-regulation function for serum-starved human fibroblasts. Heliyon 2023;9: e19238.\u003c/li\u003e\n\u003cli\u003eLUSH B, CROWLEY MF, FLETCHER E, BUCHAN JF. Total and differential protein levels in the blood and cerebrospinal fluid in rheumatoid arthritis. Ann Rheum Dis 1951;10: 153-62.\u003c/li\u003e\n\u003cli\u003eLuo WD, Wang YP, Lv J, Liu Y, Qu YQ, Xu XF, et al. Age-related self-DNA accumulation may accelerate arthritis in rats and in human rheumatoid arthritis. Nat Commun 2023;14: 4394.\u003c/li\u003e\n\u003cli\u003eZhao SS, Holmes MV, Zheng J, Sanderson E, Carter AR. The impact of education inequality on rheumatoid arthritis risk is mediated by smoking and body mass index: Mendelian randomization study. Rheumatology (Oxford) 2022;61: 2167-75.\u003c/li\u003e\n\u003cli\u003eGianfrancesco MA, Crowson CS. Where There\u0026apos;s Smoke, There\u0026apos;s a Joint: Passive Smoking and Rheumatoid Arthritis. Arthritis Rheumatol 2021;73: 2161-2.\u003c/li\u003e\n\u003cli\u003eAzizov V, Zaiss MM. Alcohol Consumption in Rheumatoid Arthritis: A Path through the Immune System. Nutrients 2021;13.\u003c/li\u003e\n\u003cli\u003eHubner M, Zaiss MM, Azizov V. Double-edged sword: Alcohol\u0026apos;s effect on rheumatoid arthritis and beyond. Joint Bone Spine 2024;91: 105626.\u003c/li\u003e\n\u003cli\u003eKarlsson T, Hadizadeh F, Rask-Andersen M, Johansson A, Ek WE. Body Mass Index and the Risk of Rheumatic Disease: Linear and Nonlinear Mendelian Randomization Analyses. Arthritis Rheumatol 2023;75: 2027-35.\u003c/li\u003e\n\u003cli\u003eHadwen B, Stranges S, Barra L. Risk factors for hypertension in rheumatoid arthritis patients-A systematic review. Autoimmun Rev 2021;20: 102786.\u003c/li\u003e\n\u003cli\u003eVanEvery H, Yang W, Su J, Olsen N, Bao L, Lu B, et al. Low-Density Lipoprotein Cholesterol and the Risk of Rheumatoid Arthritis: A Prospective Study in a Chinese Cohort. Nutrients 2022;14.\u003c/li\u003e\n\u003cli\u003eMasuko K. Glucose as a Potential Key to Fuel Inflammation in Rheumatoid Arthritis. Nutrients 2022;14.\u003c/li\u003e\n\u003cli\u003eHemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife 2018;7.\u003c/li\u003e\n\u003cli\u003eVerbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 2018;50: 693-8.\u003c/li\u003e\n\u003cli\u003eLin SH, Brown DW, Machiela MJ. LDtrait: An Online Tool for Identifying Published Phenotype Associations in Linkage Disequilibrium. Cancer Res 2020;80: 3443-6.\u003c/li\u003e\n\u003cli\u003eMyers TA, Chanock SJ, Machiela MJ. LDlinkR: An R Package for Rapidly Calculating Linkage Disequilibrium Statistics in Diverse Populations. Front Genet 2020;11: 157.\u003c/li\u003e\n\u003cli\u003eSakaue S, Kanai M, Tanigawa Y, Karjalainen J, Kurki M, Koshiba S, et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet 2021;53: 1415-24.\u003c/li\u003e\n\u003cli\u003eMbatchou J, Barnard L, Backman J, Marcketta A, Kosmicki JA, Ziyatdinov A, et al. Computationally efficient whole-genome regression for quantitative and binary traits. Nat Genet 2021;53: 1097-103.\u003c/li\u003e\n\u003cli\u003eBrown P, Pratt AG, Hyrich KL. Therapeutic advances in rheumatoid arthritis. BMJ 2024;384: e070856.\u003c/li\u003e\n\u003cli\u003eCush JJ. Rheumatoid Arthritis: Early Diagnosis and Treatment. Rheum Dis Clin North Am 2022;48: 537-47.\u003c/li\u003e\n\u003cli\u003eBugatti S, De Stefano L, Gandolfo S, Ciccia F, Montecucco C. Autoantibody-negative rheumatoid arthritis: still a challenge for the rheumatologist. Lancet Rheumatol 2023;5: e743-55.\u003c/li\u003e\n\u003cli\u003eMun S, Lee J, Park M, Shin J, Lim MK, Kang HG. Serum biomarker panel for the diagnosis of rheumatoid arthritis. Arthritis Res Ther 2021;23: 31.\u003c/li\u003e\n\u003cli\u003eWALLIS AD. The serum proteins in rheumatoid arthritis. Ann Intern Med 1950;32: 63-71.\u003c/li\u003e\n\u003cli\u003eFukuda W, Yamazaki T, Akaogi T, Hayashi H, Kusakabe T, Tsubouchi Y, et al. Malnutrition and disease progression in patients with rheumatoid arthritis. Mod Rheumatol 2005;15: 104-7.\u003c/li\u003e\n\u003cli\u003eHayashi H, Satoi K, Sato-Mito N, Kaburagi T, Yoshino H, Higaki M, et al. Nutritional status in relation to adipokines and oxidative stress is associated with disease activity in patients with rheumatoid arthritis. Nutrition 2012;28: 1109-14.\u003c/li\u003e\n\u003cli\u003eCano-Garcia L, Redondo-Rodriguez R, Manrique-Arija S, Dominguez-Quesada C, Crisostomo VJ, Armenteros-Ortiz P, et al. Prevalence of Malnutrition and Associated Factors in Older Patients with Rheumatoid Arthritis: A Cross-Sectional Study. Nutrients 2023;15.\u003c/li\u003e\n\u003cli\u003eTani K, Shimizu T, Motoki Y, Sone S. Chemokines in synovial inflammation in rheumatoid arthritis: basic and clinical aspects. Mod Rheumatol 2002;12: 93-9.\u003c/li\u003e\n\u003cli\u003eJohnson RW. Inhibition of growth by pro-inflammatory cytokines: an integrated view. J Anim Sci 1997;75: 1244-55.\u003c/li\u003e\n\u003cli\u003eBrown DL, Cooper AG, Bluestone R. Exchange of IgM and albumin between plasma and synovial fluid in rheumatoid arthritis. Ann Rheum Dis 1969;28: 644-51.\u003c/li\u003e\n\u003cli\u003eLevick JR. Permeability of rheumatoid and normal human synovium to specific plasma proteins. Arthritis Rheum 1981;24: 1550-60.\u003c/li\u003e\n\u003cli\u003eLiberatore M, Clemente M, Iurilli AP, Zorzin L, Marini M, Di Rocco E, et al. Scintigraphic evaluation of disease activity in rheumatoid arthritis: a comparison of technetium-99m human non-specific immunoglobulins, leucocytes and albumin nanocolloids. Eur J Nucl Med 1992;19: 853-7.\u003c/li\u003e\n\u003cli\u003eWallis WJ, Simkin PA, Nelp WB, Foster DM. Intraarticular volume and clearance in human synovial effusions. Arthritis Rheum 1985;28: 441-9.\u003c/li\u003e\n\u003cli\u003ePieringer H, Brummaier T, Piringer B, Auer-Hackenberg L, Hartl A, Puchner R, et al. Urinary Albumin Excretion and Vascular Function in Rheumatoid Arthritis. J Korean Med Sci 2016;31: 382-8.\u003c/li\u003e\n\u003cli\u003ePedersen LM, Nordin H, Svensson B, Bliddal H. Microalbuminuria in patients with rheumatoid arthritis. Ann Rheum Dis 1995;54: 189-92.\u003c/li\u003e\n\u003cli\u003eGarg U, Jain N, Kaul S, Nagaich U. Role of Albumin as a Targeted Drug Carrier in the Management of Rheumatoid Arthritis: A Comprehensive Review. Mol Pharm 2023;20: 5345-58.\u003c/li\u003e\n\u003cli\u003eWunder A, Muller-Ladner U, Stelzer EH, Funk J, Neumann E, Stehle G, et al. Albumin-based drug delivery as novel therapeutic approach for rheumatoid arthritis. J Immunol 2003;170: 4793-801.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"serum proteins, albumin, rheumatoid arthritis, NHANES, Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-4251713/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4251713/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eWith the increasing incidence of rheumatoid arthritis (RA) and the increasing percentage of serum RF negativity, there is an urgent need for more and more accurate methods for the early diagnosis and prevention of RA, among which serum proteins are closely related to the development of RA and are expected to become new auxiliary diagnostic tools, but their relationship with RA is not clear, so this study aimed to investigate the causal relationship between total protein (TP), albumin (ALB), globulin ( GLB), and albumin-globulin ratio (A/G) on the causal relationship of rheumatoid arthritis (RA).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this study, the relationship between TP, ALB, GLB, A/G and rheumatoid arthritis was comprehensively evaluated by generalized linear modeling and smoothed curve fitting through the data of serum proteins and RA in the NHANES(National Health and Nutrition Examination Survey) database; moreover, for the positive results with significant associations, the inverse variance weighted (IVW) method in Mendelian Randomization (MR) was used in conjunction with the other four methods to further validate and clarify the causative relationship, and finally, the results were subjected to the inspection of heterogeneity and horizontal polytomousness in order to assess whether the results were robust.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn the observational study, after correction for confounders, TP, GLB, and A/G were not significantly associated with rheumatoid arthritis, whereas ALB was significantly negatively associated with rheumatoid arthritis (OR\u0026thinsp;=\u0026thinsp;0.662, [95%CI\u0026thinsp;=\u0026thinsp;0.507\u0026ndash;0.864], P\u0026thinsp;=\u0026thinsp;0.003), and subgroup analyses showed significant negative associations in both males and females (male : OR\u0026thinsp;=\u0026thinsp;0.674, [95%CI\u0026thinsp;=\u0026thinsp;0.458\u0026ndash;0.991], P\u0026thinsp;=\u0026thinsp;0.047; females: OR\u0026thinsp;=\u0026thinsp;0.661, [95%CI\u0026thinsp;=\u0026thinsp;0.437\u0026ndash;0.999], P\u0026thinsp;=\u0026thinsp;0.049). In further MR analysis, IVW: ALB on RA, OR\u0026thinsp;=\u0026thinsp;0.70 [95%0.52\u0026ndash;0.93], P\u0026thinsp;=\u0026thinsp;0.013; RA on ALB, OR\u0026thinsp;=\u0026thinsp;0.95 [95%CI\u0026thinsp;=\u0026thinsp;0.93\u0026ndash;0.98], P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.The results of the MR analyses remained consistent with NHANES.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThere is a significant relationship between ALB and RA, and the reduction of ALB may be one of the risk factors for RA, as well as one of the outcomes in the development of RA.\u003c/p\u003e","manuscriptTitle":"Association between total protein, albumin, globulin, albumin- globulin ratio and rheumatoid arthritis: evidence from NHANES and Mendelian randomization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-16 06:41:07","doi":"10.21203/rs.3.rs-4251713/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":"32cc58d6-5c42-41c1-a01a-63d5b110d76a","owner":[],"postedDate":"April 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-21T23:58:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-16 06:41:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4251713","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4251713","identity":"rs-4251713","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}