Epigenetic Age Acceleration and Rheumatoid Arthritis: An NHANES-Based Analysis and Survival Prediction Models

Epigenetic Age Acceleration and Rheumatoid Arthritis: An NHANES-Based Analysis and Survival Prediction Models | 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 Epigenetic Age Acceleration and Rheumatoid Arthritis: An NHANES-Based Analysis and Survival Prediction Models Yuhang Ou, Zhihao Wang, Yunbo Yuan, Yuze He, Wenhao Li, Hao Ren, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6211246/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Clinical Epigenetics → Version 1 posted 8 You are reading this latest preprint version Abstract Objective : Epigenetic aging has been confirmed to be associated with the pathogenesis of rheumatoid arthritis (RA), however, its role in the prognosis of RA remains unclear. Methods : In this cross-sectional and prospective study, Epigenetic age and acceleration in participants of the National Health and Nutrition Examination Survey (NHANES) were calculated with Horvath’s clock, Hannum’s clock, PhenoAge, GrimAge, and GrimAge version 2 (GrimAge2). The association of epigenetic age and epigenetic age acceleration with the risk and mortality of RA was assessed with prediction models constructed. Results : Accelerated epigenetic ageing increased the risk of RA mortality with hazard ratio of 1.075 (95% CI 1.043 - 1.107, p <0.0001) for GrimAge2 acceleration (GrimAge2Accel) and 1.064 (1.032 - 1.098, p <0.0001) for GrimAge acceleration (GrimAgeAccel). The GrimAge2Accel-based models, adjusted for three groups of covariates, excelled in predicting the 1-year, 10-year, and 20-year survival with area under curve of 0.856 (95% CI 0.666 - 1.046), 0.871 (0.819 - 0.923), and 0.898 (0.839 - 0.956), respectively. Conclusion : Epigenetic ageing may play a harmfully promotive role in the onset and progression of RA, and the GrimAge2Accel-based prediction models could effectively predict the survival of RA patients. Further research is needed to elucidate the underlying mechanisms and to explore the potential clinical implications. Epigenetic age acceleration Rheumatoid arthritis NHANES Cross-sectional and prospective study Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Rheumatoid arthritis (RA) is a complex autoimmune disease characterized by chronic joint inflammation and accompanied by multi-systemic damage [ 1 ]. Globally, approximately one out of every 200 people is affected by RA, with 2–3 times higher incidence in females than in males [ 1 ]. The etiology and pathogenesis of RA are not yet fully understood, despite genetics, environmental factors, and immune dysregulation have been found to potentially play roles in disease progression [ 2 ]. Currently, genetics is recognized as one of the most prominent risk factors for RA, encompassing both genetic and epigenetic risks [ 3 ]. The most direct evidence of genetic risk for RA is that first-degree relatives of RA patients have 2 to 5-fold higher risk of disease compared to whom of non-RA controls [ 4 ]. Epigenetic factor could also contribute to RA risk. Compared to monogenic diseases, the concordance rate for RA prevalence among monozygotic twins is relatively low, at approximately 15% [ 5 ], and prevalence-discordant monozygotic twins of RA exhibit different DNA methylation (DNAm) patterns [ 6 ]. Besides, changes in histone modifications, X-chromosome activity, and non-coding RNA expression patterns were all observed in RA patients [ 7 ]. These suggest that epigenetics may be a key regulatory mechanism in the pathological processes of RA, providing new insights for exploring therapeutic and preventive strategies. Although RA can occur at any age, it typically peaks in older age, suggesting aging as one of the hallmarks of RA [ 8 ]. In the aspect of biology, aging does not solely refer to the increase in chronological age, more precisely, it refers to age-dependent functional decline [ 9 ], a complex biological process characterized by the gradual decline of bodily functions and an increased risk of multi-system disease [ 10 ]. Intriguingly, aging-associated phenotypes, including immunosenescence, cellular senescence, and telomere shortening, are all potential mechanisms involved in the pathogenesis of RA [ 8 ]. Similar phenomenon of immune system aging can be observed in both aging individuals and RA patients, particularly the enhanced innate immune response and the diminished adaptive immune response [ 8 ], which suggests that aging may contribute to the initiation and progression of RA through alterations in the immune system. In addition, age-related comorbidities can also affect the course of autoimmune diseases [ 8 ]. Depression is the most common comorbidity in RA [ 11 ], and a cohort study has demonstrated a significant elevation in RA risk with depression [ 12 ]. Overall, how aging might induce RA was still not completely clear. While chronological aging is uniform across individuals, the speed of functional decline exhibits considerable heterogeneity [ 13 ]. To better define aging-related biological decline, several biomarkers have been used, including DNAm, telomere length, transcriptomics, proteomics, metabolomics and composite biomarker panels [ 14 ]. As one of the most promising predictors, DNAm was able to reflect both genetic and environmental characteristics of individuals [ 15 ]. As an epigenetic modification, DNAm typically occurs in DNA regions rich in cytosine-phosphate-guanine (CpG) dinucleotides, where the cytosines become covalently linked to methyl groups, leading to methylation [ 16 ]. Changes in certain DNAm sites correlate linearly with age, for instance, CpG sites (CpGs) in promoter regions often undergo hypermethylation during aging, while other CpGs shift to a hypomethylated state [ 17 ]. This kind of biological aging based on changes in DNAm patterns, known as epigenetic aging, is considered a more accurate reflection of an organism's biological age and health status than chronological age[ 18 ]. Existing studies have developed methods to measure epigenetic ageing, referred to as epigenetic clocks [ 19 – 22 ], utilizing DNAm data to estimate telomere length and integrating other aging markers through the inclusion of clinical, lifestyle, and immune biomarkers, the interpretability of which has been supported by reliable findings [ 21 ]. As for epigenetic clocks and RA risk, previous studies have reported that RA patients exhibit higher epigenetic age acceleration compared to non-RA controls [ 23 ]. Additionally, significant changes in DNAm patterns have been observed in specific tissues, such as synovium and immune cells [ 24 ], suggesting that epigenetic aging may play a key role in the onset and progression of RA. To explore the role of epigenetic aging in RA, particularly its relationship with RA prognosis, in this cross-sectional and prospective study, we utilized data from the National Health and Nutrition Examination Survey (NHANES), analyzing the association of five well-validated epigenetic clocks with both prevalence and mortality risk of RA and established prediction models for short-, mid-, and long-term survival probabilities accordingly. Our findings could shed light on the value of epigenetic clocks in the prognosis and survival prediction of RA. Methods Study design and population In this cross-sectional and prospective study, we used data from the NHANES to assess their survival prediction value of five measures of epigenetic age acceleration on participants with RA. Protocols of the NHANES have been approved by The National Center for Health Statistics ethics review board. Subjects from the NHANES 1999 to 2002 were enrolled, as their epigenetic biomarkers were available. Our inclusion and exclusion strategy were displayed in Figure 1 . In the DNAm dataset, a total of 4,449 participants aged over 50 years were documented, among which 2,532 had data on epigenetic biomarkers, and none of them were excluded due to ineligible mortality follow-up. Ultimately, a total of 2,248 participants without RA and 284 RA patients without missing information were analyzed in this study. Mortality outcome The mortality of subjects was from the National Center for Health Statistics. Only participants with eligible follow-up were used for survival analysis. Eligible participants were coded as 0 (assumed alive) and 1 (assumed deceased). The follow-up time was measured as month from the date on the NHANES interview or mobile examination center, in which the larger number was applied as the survival time. Complete description of mortality data methodology was available at: https://www.cdc.gov/nchs/data-linkage/mortality-public.htm. Epigenetic clocks and epigenetic age acceleration We used five commonly applied and well-validated epigenetic age acceleration measures, referred to as epigenetic clocks, to assess epigenetic aging in all participants: Horvath’s epigenetic clock, Hannum’s epigenetic clock, PhenoAge, GrimAge, and GrimAge version 2 (GrimAge2). Horvath’s clock and Hannum’s clock are known as the first generation of epigenetic clocks. Horvath’s clock is the first multi-tissue age estimator, based on DNAm data from multiple tissues or cell types at different stages of life, from which 353 age-associated CpGs are selected to construct a predictive model of epigenetic age [19]. Hannum’s clock, constructed using DNAm data from 71 age-related CpGs in adult whole blood samples [20], demonstrates higher accuracy in estimating chronological age compared to Horvath’s clock when applied to adult blood samples, but shows considerable bias when applied to non-blood tissues or in children [25]. PhenoAge and GrimAge are considered second-generation epigenetic clocks. In addition to using DNAm data, they incorporate various biomarkers and clinical features, focusing more on predicting biological age and the incidence of adverse outcomes [15]. PhenoAge integrates chronological age and nine mortality-associated biomarkers as composite predictor variables, employing regression analysis on DNAm data from 513 CpGs in whole blood samples to predict "phenotypic age" rather than chronological age [21]. GrimAge, constructed based on a series of plasma protein biomarkers, smoking pack-years and 1,030 CpGs, regresses time-to-death on the biomarkers, whose focus on lifestyle factors and disease outcomes makes it a better predictor of individual lifespan and mortality risk [22, 26]. The biological age of an individual can be comprehensively assessed from the perspective of DNAm using the epigenetic age calculated by Horvath’s clock (HorvathAge) and Hannum’s clock (HannumAge), as well as PhenoAge and GrimAge [26-29]. Building on GrimAge, the new version, GrimAge2, adopts another two DNAm-based log transformed estimators of plasma proteins and outperforms GrimAge in predicting mortality across multiple racial population and the onset of various age-related diseases [30]. All five measures were directly obtained from the DNAm dataset, with description of sample selection, laboratory test, data pre-processing and biomarker generation available from https://wwwn.cdc.gov/nchs/nhanes/dnam/. Epigenetic age acceleration was defined as the residual value computed by a certain epigenetic age relative to chronological age, where a positive result of age acceleration indicated accelerated aging (age acceleration greater than 0), the converse would refer to non-accelerated aging (age acceleration less than or equal to 0). Assessment of RA The definition of RA was based on the Medical Conditions Questionnaire of the NHANES that two arthritis-related questions were included: “Has a doctor or other health professional ever told you that you have arthritis?” Those who answered affirmatively were then asked further onwards, “Which type of arthritis was it?” The participants who answered “rheumatoid arthritis” were identified as RA patients [31]. Covariates We controlled a series of factors that potentially affected the RA outcome as covariates in the Cox proportional hazards regression model and logistic regression models: age (continuous), sex (female, male), race (non-Hispanic black, non-Hispanic white, other Hispanic, other race - including multi-racial), smoking status (current, former, never), alcohol intake (current, former, never), body mass index (BMI, kg/m 2 , continuous), vigorous activity (yes, no), moderate activity (yes, no), poverty-income ratio (PIR, continuous), education level (9-11th grade - including 12th grade with no diploma, high school grade/GED or equivalent, some college or AA degree, college graduate or above), diabetes (yes, no), hypertension (yes, no), marital status (never married, separated, divorced, widowed). Statistical analysis R version 4.4.0 (https://www.r-project.org/) was used for statistical analysis and visualization of results. In data cleaning, any record of “Refuse to answer” and “Don't know” were labeled as missing value. In descriptive statistics, continuous variables were described as mean ± standard deviation (SD) and compared by t test or one-way ANOVA test, while categorical variables as frequency or percentage and its difference tested using chi-square test. Pearson correlation analysis was used to calculate the correlation coefficient (r), its confidence interval (CI) and p value were given by Fisher's Z transform. Restricted cubic splines (RCS) were used to examine the potential nonlinear dose-response relationship between epigenetic age and RA risk. Cox proportional hazards regression model and logistic regression models were used for survival analysis and prediction, with hazard ratio (HR) and 95% CI used to describe the mortality risk, analysis of receiver operating characteristic (ROC) curves was performed and the area under the curve (AUC) was utilized to evaluate the predictive efficiency of survival probability of 1, 10, and 20 years. R package “rms” version 6.8.1 (https://hbiostat.org/r/rms/) and “survival” version 3.5.8 (https://github.com/therneau/survival) were applied for survival analysis and prediction. Two-paired P value of 0.05 was set as threshold of statistically significance. Results Characteristics of Study Participants At baseline of this study, 284 of the 2,532 participants have reported being diagnosed with RA. Compared to participants without RA, RA patients were older (non-RA: 65.95 vs RA: 67.57, p = 0.011), and had a higher proportion of female individuals (59.5% vs 48.0%, p < 0.001), and more likely to have a higher BMI, a lower PIR, and a lower education level. There were more deceased individuals (52.7% vs 62.0%, p = 0.004) in RA patients than participants without RA, potentially related to the difference in overall follow-up time between the two groups (169.67 months vs 160.08 months, p = 0.033). The baseline characteristics of all participants are presented in Table 1 . Table 1 Baseline characteristics of participants by rheumatoid arthritis status. Characteristic Non-RA (n = 2,248) RA (n = 284) p -value Sex a < 0.001 Female 1,078 (48.0%) 169 (59.5%) Male 1,170 (52.0%) 115 (40.5%) Age, years b 65.95 (10.12) 67.57 (9.64) 0.011 Race a < 0.001 Mexican American 645 (28.7%) 76 (26.8%) Non-Hispanic Black 447 (19.9%) 91 (32.0%) Non-Hispanic White 931 (41.4%) 96 (33.8%) Other Hispanic 149 (6.6%) 14 (4.9%) Other Race - Including Multi-Racial 76 (3.4%) 7 (2.5%) Smoking status a 0.086 Current 334 (14.9%) 54 (19.1%) Former 875 (39.0%) 95 (33.6%) Never 1,034 (46.1%) 134 (47.3%) Alcohol intake a 0.003 Current 1,366 (63.7%) 140 (53.2%) Former 412 (19.2%) 61 (23.2%) Never 366 (17.1%) 62 (23.6%) BMI, kg/m 2 b 28.47 (5.70) 30.37 (6.49) < 0.001 Vigorous activity a 0.673 No 1,839 (81.9%) 236 (83.1%) Yes 407 (18.1%) 48 (16.9%) Moderate activity a 0.004 No 1,416 (63.0%) 204 (71.8%) Yes 830 (37.0%) 80 (28.2%) PIR b 2.64 (1.60) 2.12 (1.47) < 0.001 Education level a < 0.001 Less than 9th Grade 592 (26.4%) 102 (35.9%) 9-11th Grade (Including 12th grade with no diploma) 400 (17.8%) 70 (24.6%) High School Grad/ GED or Equivalent 458 (20.4%) 58 (20.4%) Some College or AA degree 431 (19.2%) 34 (12.0%) College Graduate or above 363 (16.2%) 20 (7.0%) Diabetes a 0.529 No 1,849 (82.3%) 228 (80.6%) Yes 398 (17.7%) 55 (19.4%) Hypertension a 0.851 No 1,248 (58.0%) 160 (58.8%) Yes 903 (42.0%) 112 (41.2%) Marital status a 0.1 Married/ Living with partner 1,389 (64.5%) 158 (59.2%) Never married/ Separated/ Divorced/ Widowed 764 (35.5%) 109 (40.8%) Mortality status a 0.004 False/ Survived 1,063 (47.3%) 108 (38.0%) True/ Deceased 1,185 (52.7%) 176 (62.0%) OS, months b 169.67 (71.18) 160.08 (74.06) 0.033 a Statistics were shown as N (%) b Statistics were shown as mean (SD) PIR = poverty income ratio. RA = rheumatoid arthritis. Association of epigenetic age and epigenetic age acceleration with risk of RA To thoroughly test the association of epigenetic age and epigenetic age acceleration with the risk of RA, we used the five different age measures mentioned above to estimate the epigenetic age of RA patients and non-RA participants, and calculated the corresponding age accelerations, including HorvathAge acceleration (HorvathAgeAccel), HannumAge acceleration (HannumAgeAccel), PhenoAge acceleration (PhenoAgeAccel), GrimAge acceleration (GrimAgeAccel), and GrimAge2 acceleration (GrimAge2Accel), five of those ten indicators were significantly associated with RA (Table 2 ). We identified a negative association between HannumAgeAccel and RA that RA patients exhibited a decreased rate of aging compared with non-RA cases by 0.84 years (non-RA: 1.05 vs RA: 0.21, p = 0.032). In contrast, HorvathAge, PhenoAge, GrimAge, and GrimAge2 positively associated with RA. RA patients showed an increase rate of aging compared with non-RA participants by 1.35 years for HorvathAge (non-RA: 66.75 vs RA: 68.01, p = 0.034), 1.93 years for PhenoAge (55.58 vs 57.51, p = 0.005), 1.75 years for GrimAge (66.19 vs 67.94, p = 0.002), and 2.02 years for GrimAge2 (71.98 vs 74.00, p < 0.001), respectively. HorvathAgeAccel, HannumAge, PhenoAgeAccel, GrimAgeAccel, and GrimAge2Accel did not demonstrate statistically significant association with RA prevalence. Table 2 Association of epigenetic age and epigenetic age acceleration with risk of RA. Epigenetic aging marker a Non-RA (n = 2,248) RA (n = 284) Δ Value (RA - Non-RA) p -value HorvathAge 66.75 (9.47) 68.01 (8.92) 1.35 0.034 HorvathAgeAccel 0.79 (6.36) 0.44 (5.85) -0.35 0.368 HannumAge 67.00 (9.96) 67.78 (9.51) 0.78 0.212 HannumAgeAccel 1.05 (6.26) 0.21 (5.63) -0.84 0.032 PhenoAge 55.58 (11.06) 57.51 (10.78) 1.93 0.005 PhenoAgeAccel -10.37 (7.31) -10.06 (7.55) 0.31 0.496 GrimAge 66.19 (8.94) 67.94 (8.47) 1.75 0.002 GrimAgeAccel 0.23 (5.50) 0.37 (5.40) 0.14 0.696 GrimAge2 71.98 (8.85) 74.00 (8.33) 2.02 < 0.001 GrimAge2Accel 6.03 (6.05) 6.43 (5.91) 0.40 0.286 a Statistics were shown as mean (SD) HorvathAgeAccel = HorvathAge acceleration. HannumAgeAccel = HannumAge acceleration. PhenoAgeAccel = PhenoAge acceleration. GrimAgeAccel = GrimAge acceleration. GrimAge2Accel = GrimAge2 acceleration With comparatively stronger statistical significance, GrimAge2 and GrimAge were further selected for RCS analysis, which indicated that higher GrimAge2 ( p -non-linear = 0.192, p -overall = 0.002) and higher GrimAge ( p -non-linear = 0.154, p -overall = 0.008) were linearly associated with the risk of RA (Fig. 2 ). Collectively, we found that the risk of RA is generally associated with epigenetic age, particularly showing a positive linear dose-response relationship with GrimAge2 and GrimAge in this part. Association between epigenetic age acceleration and RA mortality To verify the association between epigenetic age acceleration and RA mortality, Cox proportional hazards regression model was used to assess the impact of five epigenetic aging indicators on RA outcome. As age and sex affect both epigenetic age acceleration [ 32 ] and RA [ 33 ], the association was adjusted by incorporating age and sex as covariates in the model. As shown in Fig. 3 , in the NHANES 1999 to 2002, HorvathAgeAccel, HannumAgeAccel, and PhenoAgeAccel had no statistically significant effect on RA outcome (all p > 0.05). However, GrimAgeAccel and GrimAge2Accel significantly increased the risk of mortality in participants with RA. For each one-year increase in GrimAgeAccel, the risk of RA mortality increased by 6.4% (HR 1.064 [95% CI 1.032–1.098], p < 0.0001), whereas each one-year increase in GrimAge2Accel increased the risk by 7.5% (1.075 [1.043–1.107], p < 0.0001). Therefore, GrimAgeAccel and GrimAge2Accel were selected to construct the predictive models for RA survival in the next step. Survival prediction of RA mortality with epigenetic age acceleration To assess the predictive efficiency of epigenetic age acceleration for RA mortality, firstly, the multivariable Cox regression model was conducted to evaluate the independent effects of each covariate on the survival outcome of participants with RA. As detailed in Table 3 , in the NHANES 1999 to 2002, age, race, smoking status, education level, and diabetes were identified as risk factors independently associated with RA outcome and incorporated into one of the models of multivariable logistic regression analysis. Table 3 Association of covariates with RA mortality. Covariate Coefficient HR (95% CI) p -value Age 0.090 1.094 (1.0665–1.121) < 0.0001 Sex Female Ref Ref Ref Male 0.322 1.380 (0.8509–2.238) 0.192 Race Mexican American Ref Ref Ref Non-Hispanic Black 0.434 1.544 (0.8529–2.795) 0.151 Non-Hispanic White 0.829 2.290 (1.2546–4.180) 0.007 Other Hispanic -2.032 0.131 (0.0171–1.004) 0.051 Other Race - Including Multi-Racial 1.634 5.122 (1.3464–19.487) 0.017 Smoking Status Current Ref Ref Ref Former -0.743 0.476 (0.2749–0.823) 0.008 Never -1.035 0.355 (0.2001–0.630) < 0.001 Alcohol intake Current Ref Ref Ref Former -0.006 0.994 (0.5884–1.678) 0.981 Never 0.157 1.170 (0.6465–2.119) 0.603 BMI 0.014 1.015 (0.9849–1.045) 0.341 Vigorous activity No Ref Ref Ref Yes -0.390 0.677 (0.3637–1.259) 0.218 Moderate activity No Ref Ref Ref Yes -0.184 0.832 (0.5189–1.335) 0.446 PIR -0.110 0.896 (0.7607–1.055) 0.189 Education level Less than 9th Grade Ref Ref Ref 9-11th Grade (Including 12th grade with no diploma) -0.592 0.553 (0.3110–0.983) 0.044 High School Grad/ GED or Equivalent -0.338 0.713 (0.3877–1.311) 0.276 Some College or AA degree -0.681 0.506 (0.2423–1.057) 0.070 College Graduate or above -0.280 0.756 (0.3005–1.901) 0.552 Diabetes No Ref Ref Ref Yes 0.603 1.828 (1.0932–3.057) 0.022 Hypertension No Ref Ref Ref Yes 0.234 1.264 (0.8196–1.948) 0.290 Marital status Married/ Living with partner Ref Ref Ref Never married/ Separated/ Divorced/ Widowed 0.189 1.208 (0.7793–1.871) 0.399 HR = hazard ratio. PIR = poverty income ratio. Three logistic regression models were subsequently established to analyze the predictive efficiency of GrimAge2Accel for survival of RA patients: Model 1 was adjusted for age and sex, Model 2 was adjusted for the independent risk factors of RA outcome as described above, and Model 3 was adjusted for all covariates included in this study. The cut-off points of survival time were set as 1 year, 10 years, and 20 years, representing short-term, medium-term, and long-term survival, respectively. Nomograms for survival prediction of RA patients were constructed by combining the RA prognosis-related factors from each model with their respective weight values (Fig. 4 ), and their corresponding calibration curves were plotted, which showed good consistency between the predicted survival and the actual survival, indicating no significant deviation between the predictions and actual outcome (Fig. 5 A-C). As shown in Fig. 5 D-F, Model 2 was the most accurate in predicting the 1-year survival probability of RA (AUC 0.856 [95% CI 0.666–1.046]), while Model 3 excelled in predicting the 10-year (0.871 [0.819–0.923]) and 20-year (0.898 [0.839–0.956]) survival probability of RA. In addition, we employed the same approach to evaluate the predictive performance of GrimAgeAccel, which showed similar results to GrimAge2Accel. Specifically, GrimAgeAccel adjusted for independent risk factors was the best model for predicting 1-year survival probability (AUC 0.844 [95% CI 0.652–1.036]), and when adjusted for all covariates, it exhibited the highest AUCs for the 10-year and 20-year survival probabilities (0.864 [0.809–0.918] and 0.889 [0.826–0.952], respectively). Taken together, the GrimAge2Accel-based and GrimAgeAccel-based prediction models could effectively predict the survival of RA patients. The complete evaluation results of the three models derived from GrimAgeAccel are presented in Figure S1 and Figure S2 . Each variable axis represents a predictor factor, with the coordinates above the axis indicating the values of the variable. The corresponding point value is used to quantify the effect of that variable on the prediction outcome. The total points are obtained by summing the point values of each variable, with higher total points representing a lower survival probability for RA patients (1-year, 10-year, and 20-year survival). GrimAge2Accel = GrimAge2 acceleration. BMI = body mass index. PIR = poverty income ratio. Discussion This study explored the association of epigenetic clocks with RA and its mortality risk through cross-sectional and prospective analyses. We found that increased HorvathAge, PhenoAge, GrimAge, and GrimAge2 were significantly associated with RA. Studies by Chen Li et al. and Mukherjee et al. also found that the biological age or epigenetic age of RA patients was higher than that of healthy controls [ 23 , 34 ]. However, it is noteworthy that among the participants included in this study, HannumAgeAccel showed a negative correlation with the risk of RA. The data for Hannum's clock is derived solely from CpGs in adult blood samples without other clinical biomarkers or lifestyle factors included in the training, thus the cross-tissue applicability of which is limited, resulting in bias when applied to non-blood tissues [ 25 ]. When Mukherjee et al. applied Hannum’s clock to the RA cohort, they also did not observe a correlation between HannumAge and RA [ 23 ]. Although in the study by Kresovich et al., HannumAgeAccel was statistically significantly associated with an increased risk of breast cancer [ 35 ], the methylation markers carried by ctDNA in blood are tumor-specific and used for transmitting epigenetic information [ 36 ]. The association between HannumAge and RA should be further verified. More importantly, we found that GrimAge2Accel and GrimAgeAccel were significantly associated with poor prognosis in RA, consistent with previous studies showing their association with all-cause mortality in multiple cohorts [ 22 , 30 , 37 ]. When different covariates were included, the predictive models based on GrimAge2Accel and GrimAgeAccel showed all AUCs greater than 0.8, suggesting that these models could effectively predict the short-, medium-, and long-term survival probabilities of RA patients. Overall, our results suggest that increased epigenetic age is associated with RA, and the acceleration of epigenetic age may help identify the risk of mortality in RA patients. The predictive models built on this basis can serve as reliable predictors of RA survival probability. As for the pathogenesis of RA, researches have gradually shifted from a focus on immune abnormalities to a more integrated, multi-layered approach, including Immunological abnormalities, specific inflammatory pathway, metabolic disorders, genetic susceptibility, and epigenetic regulation-a new area of research [ 38 ]. Early studies have shown that T cells and fibroblast-like synovial cells in RA patients’ joint biopsy samples exhibit significant DNA hypomethylation [ 39 , 40 ]. Similar observations were made in the IL-6 promoter region of peripheral blood mononuclear cells [ 41 ], accompanied by low expression of DNA methyltransferase 1 (DNMT1) and high expression of ten-eleven translocation protein 1 (TET1), TET2 and TET3 that involved in demethylation [ 42 ]. However, the promoter region of the CTLA-4 in regulatory T cells (Treg) underwent hypermethylation [ 43 ]. These inconsistent methylation changes are, to some extent, similar to the differential methylation patterns observed in aging [ 17 ]. This phenomenon may occur because the inflammatory driving cells and effector cells mentioned above could lower methylation levels through the decreased expression of methyltransferases and the increased expression of demethylases, thereby upregulating the expression of inflammation-related genes and promoting the chronic inflammatory of RA [ 44 ]. Under chronic inflammation, abnormal immune responses may impair DNA damage repair mechanisms, contributing to altered DNAm patterns [ 45 ]. Additionally, chronic inflammation is also responsible for iron buildup in the liver [ 46 ], which has been reported to be associated with epigenetic age acceleration [ 47 ]. In the abnormal methylation states, high methylation of NF-AT binding site in the CTLA-4 gene promoter of Treg cells results in reduced CTLA-4 expression, and, consequently, Treg cells cannot induce the expression and activation of indoleamine 2,3-dioxygenase, a tryptophan-degrading enzyme, and the kynurenine pathway remains inactive [ 43 ], in which manner, dysfunctional Treg cells fail to maintain immune balance, thus exacerbating systemic chronic inflammation. Thus, it is possible that methylation erasure of specific DNA sites in inflammation-related cells trigger the onset of chronic inflammation, which might promote the progression of RA, and induce abnormal epigenetic modifications. Methylation patterns alteration in certain pathway leads to immune dysregulation and finally results in unpleasant prognosis, which could be validated in further studies. In recent years, with the increasing life expectancy, the number of RA cases, age-standardized prevalence, and the disability-adjusted life years have been steadily rising worldwide, posing a significant disease burden [ 48 ]. As alterations in DNAm patterns have been confirmed to play a potential role in the onset of RA, greater attention should be paid to the value of DNAm in predicting and intervening in RA prognosis, and, the strength of this study lies in its comprehensive analysis of RA and its poor prognosis with multiple epigenetic clocks, leading to the development of reliable RA survival probability prediction models. The main limitation of this study is that, although we have demonstrated the association of epigenetic aging with RA and its outcome, our observational study cannot establish specific causal relationship, or the mechanisms involved. However, the relatively small sample size limits the extrapolation to other populations. To overcome this limitation, future studies should consider expanding the sample size. The predictive models established in this study also need to be validated for robustness in other cohorts. In addition, although we have considered possible covariates in advance, we still cannot exclude the potential impact of other unknown factors on the statistical results. Conclusion In conclusion, the prediction models based on GrimAge2Accel and GrimAgeAccel could effectively predict the survival of RA patients, suggesting that epigenetic aging may play a promotive role in the onset and progression of RA. Identifying changes in biological markers associated with epigenetic aging may provide potential guidance for RA mortality prediction and prevention. Further researches are still needed to elucidate the underlying mechanisms and explore broader clinical implications. Declarations Data availability Data was available from NHANES website via www.cdc.gov/nchs/nhanes/index.htm. Acknowledgements Our sincere thanks go to all participants and researchers in collecting and building NHANES, and developers and supporters of relevant software and packages. Funding This study was fully supported by the Sichuan Science and Technology Program (2023YFQ0002 to Yanhui Liu). Author contributions Zhihao Wang and Yanhui Liu designed the study. Yuhang Ou, Zhihao Wang, Yunbo Yuan, Yuze He, Wenhao Li, Hao Ren, Junhong Li, and Siliang Chen collected, analyzed and interpreted the data. Yuhang Ou and Zhihao Wang drafted the initial manuscript. All authors revised the manuscript. All authors reviewed the final manuscript and approved the submission. Ethics approval and consent to participate This study was reviewed and approved by the National Center for Health Statistics ethics review board. All human subjects provided written informed consent when participating. Conflict of interest We declare no competing interests. References Smith MH, Berman JR: What Is Rheumatoid Arthritis? JAMA 2022, 327(12):1194–1194. Littlejohn EA, Monrad SU: Early Diagnosis and Treatment of Rheumatoid Arthritis. Primary Care: Clinics in Office Practice 2018, 45(2):237–255. Gravallese Ellen M, Firestein Gary S: Rheumatoid Arthritis — Common Origins, Divergent Mechanisms. New England Journal of Medicine 2023, 388(6):529–542. Viatte S, Plant D, Han B, Fu B, Yarwood A, Thomson W, Symmons DPM, Worthington J, Young A, Hyrich KL et al : Association of HLA-DRB1 Haplotypes With Rheumatoid Arthritis Severity, Mortality, and Treatment Response. JAMA 2015, 313(16):1645–1656. SILMAN AJ, MACGREGOR AJ, THOMSON W, HOLLIGAN S, CARTHY D, FARHAN A, OLLIER WER: TWIN CONCORDANCE RATES FOR RHEUMATOID ARTHRITIS: RESULTS FROM A NATIONWIDE STUDY. Rheumatology 1993, 32(10):903–907. Svendsen AJ, Gervin K, Lyle R, Christiansen L, Kyvik K, Junker P, Nielsen C, Houen G, Tan Q: Differentially Methylated DNA Regions in Monozygotic Twin Pairs Discordant for Rheumatoid Arthritis: An Epigenome-Wide Study. Frontiers in Immunology 2016, 7. Barik RR, Bhatt LK: Emerging epigenetic targets in rheumatoid arthritis. Rheumatology International 2021, 41(12):2047–2067. Boots AMH, Maier AB, Stinissen P, Masson P, Lories RJ, De Keyser F: The influence of ageing on the development and management of rheumatoid arthritis. Nature Reviews Rheumatology 2013, 9(10):604–613. Seale K, Horvath S, Teschendorff A, Eynon N, Voisin S: Making sense of the ageing methylome. Nature Reviews Genetics 2022, 23(10):585–605. Tian YE, Cropley V, Maier AB, Lautenschlager NT, Breakspear M, Zalesky A: Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality. Nature Medicine 2023, 29(5):1221–1231. Degboe Y, Vastert SJ, Prakken BJ, McInnes IB: How does age determine the development of human immune-mediated arthritis? Nature Reviews Rheumatology 2022, 18(9):501–512. Vallerand IA, Lewinson RT, Frolkis AD, Lowerison MW, Kaplan GG, Swain MG, Bulloch AGM, Patten SB, Barnabe C: Depression as a risk factor for the development of rheumatoid arthritis: a population-based cohort study. RMD Open 2018, 4(2):e000670. Partridge L, Deelen J, Slagboom PE: Facing up to the global challenges of ageing. Nature 2018, 561(7721):45–56. Jylhävä J, Pedersen NL, Hägg S: Biological Age Predictors. EBioMedicine 2017, 21:29–36. Duan R, Fu Q, Sun Y, Li Q: Epigenetic clock: A promising biomarker and practical tool in aging. Ageing Research Reviews 2022, 81:101743. Day K, Waite LL, Thalacker-Mercer A, West A, Bamman MM, Brooks JD, Myers RM, Absher D: Differential DNA methylation with age displays both common and dynamic features across human tissues that are influenced by CpG landscape. Genome Biology 2013, 14(9):R102. Saul D, Kosinsky RL: Epigenetics of Aging and Aging-Associated Diseases. International Journal of Molecular Sciences 2021, 22(1):401. Horvath S, Raj K: DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nature Reviews Genetics 2018, 19(6):371–384. Horvath S: DNA methylation age of human tissues and cell types. Genome Biology 2013, 14(10):3156. Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, Klotzle B, Bibikova M, Fan JB, Gao Y et al : Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell 2013, 49(2):359–367. Levine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, Hou L, Baccarelli AA, Stewart JD, Li Y et al : An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY) 2018, 10(4):573–591. Lu AT, Quach A, Wilson JG, Reiner AP, Aviv A, Raj K, Hou L, Baccarelli AA, Li Y, Stewart JD et al : DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging (Albany NY) 2019, 11(2):303–327. Mukherjee N, Harrison TC: Epigenetic Aging and Rheumatoid Arthritis. The Journals of Gerontology: Series A 2023, 79(1). Nakano K, Whitaker J, Boyle D, Wang W, Firestein G: DNA methylome signature in rheumatoid arthritis. Annals of the Rheumatic Diseases 2012, 72:110–117. Horvath S, Oshima J, Martin GM, Lu AT, Quach A, Cohen H, Felton S, Matsuyama M, Lowe D, Kabacik S et al : Epigenetic clock for skin and blood cells applied to Hutchinson Gilford Progeria Syndrome and ex vivo studies. Aging (Albany NY) 2018, 10(7):1758–1775. McCrory C, Fiorito G, Hernandez B, Polidoro S, O’Halloran AM, Hever A, Ni Cheallaigh C, Lu AT, Horvath S, Vineis P et al : GrimAge Outperforms Other Epigenetic Clocks in the Prediction of Age-Related Clinical Phenotypes and All-Cause Mortality. The Journals of Gerontology: Series A 2020, 76(5):741–749. Hillary RF, Stevenson AJ, McCartney DL, Campbell A, Walker RM, Howard DM, Ritchie CW, Horvath S, Hayward C, McIntosh AM et al : Epigenetic measures of ageing predict the prevalence and incidence of leading causes of death and disease burden. Clin Epigenetics 2020, 12(1):115. Yusipov I, Kalyakulina A, Trukhanov A, Franceschi C, Ivanchenko M: Map of epigenetic age acceleration: A worldwide analysis. Ageing Research Reviews 2024, 100:102418. Liu Z, Kuo PL, Horvath S, Crimmins E, Ferrucci L, Levine M: A new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES IV: A cohort study. PLoS Med 2018, 15(12):e1002718. Lu AT, Binder AM, Zhang J, Yan Q, Reiner AP, Cox SR, Corley J, Harris SE, Kuo PL, Moore AZ et al : DNA methylation GrimAge version 2. Aging (Albany NY) 2022, 14(23):9484–9549. Chen L, Sun Q, Peng S, Tan T, Mei G, Chen H, Zhao Y, Yao P, Tang Y: Associations of blood and urinary heavy metals with rheumatoid arthritis risk among adults in NHANES, 1999–2018. Chemosphere 2022, 289:133147. Crimmins EM, Thyagarajan B, Levine ME, Weir DR, Faul J: Associations of Age, Sex, Race/Ethnicity, and Education With 13 Epigenetic Clocks in a Nationally Representative U.S. Sample: The Health and Retirement Study. The Journals of Gerontology: Series A 2021, 76(6):1117–1123. Pertsinidou E, Manivel VA, Westerlind H, Klareskog L, Alfredsson L, Mathsson-Alm L, Hansson M, Saevarsdottir S, Askling J, Rönnelid J: Rheumatoid arthritis autoantibodies and their association with age and sex. Clin Exp Rheumatol 2021, 39(4):879–882. Chen L, Wu B, Mo L, Chen H, Zhao Y, Tan T, Chen L, Li Y, Yao P, Tang Y: Associations between biological ageing and the risk of, genetic susceptibility to, and life expectancy associated with rheumatoid arthritis: a secondary analysis of two observational studies. The Lancet Healthy Longevity 2024, 5(1):e45-e55. Kresovich JK, Xu Z, O’Brien KM, Weinberg CR, Sandler DP, Taylor JA: Methylation-Based Biological Age and Breast Cancer Risk. JNCI: Journal of the National Cancer Institute 2019, 111(10):1051–1058. Terp SK, Stoico MP, Dybkær K, Pedersen IS: Early diagnosis of ovarian cancer based on methylation profiles in peripheral blood cell-free DNA: a systematic review. Clinical Epigenetics 2023, 15(1):24. Hillary RF, Stevenson AJ, McCartney DL, Campbell A, Walker RM, Howard DM, Ritchie CW, Horvath S, Hayward C, McIntosh AM et al : Epigenetic measures of ageing predict the prevalence and incidence of leading causes of death and disease burden. Clinical Epigenetics 2020, 12(1):115. Gao Y, Zhang Y, Liu X: Rheumatoid arthritis: pathogenesis and therapeutic advances. MedComm (2020) 2024, 5(3):e509. Richardson B, Scheinbart L, Strahler J, Gross L, Hanash S, Johnson M: Evidence for impaired t cell dna methylation in systemic lupus erythematosus and rheumatoid arthritis. Arthritis & Rheumatism 1990, 33(11):1665–1673. Karouzakis E, Gay RE, Michel BA, Gay S, Neidhart M: DNA hypomethylation in rheumatoid arthritis synovial fibroblasts. Arthritis & Rheumatism 2009, 60(12):3613–3622. Nile CJ, Read RC, Akil M, Duff GW, Wilson AG: Methylation status of a single CpG site in the IL6 promoter is related to IL6 messenger RNA levels and rheumatoid arthritis. Arthritis & Rheumatism 2008, 58(9):2686–2693. de Andres MC, Perez-Pampin E, Calaza M, Santaclara FJ, Ortea I, Gomez-Reino JJ, Gonzalez A: Assessment of global DNA methylation in peripheral blood cell subpopulations of early rheumatoid arthritis before and after methotrexate. Arthritis Research & Therapy 2015, 17(1):233. Cribbs AP, Kennedy A, Penn H, Read JE, Amjadi P, Green P, Syed K, Manka SW, Brennan FM, Gregory B et al : Treg cell function in rheumatoid arthritis is compromised by ctla-4 promoter methylation resulting in a failure to activate the indoleamine 2,3-dioxygenase pathway. Arthritis Rheumatol 2014, 66(9):2344–2354. Rodríguez-Ubreva J, De La Calle-Fabregat C, Li T, Ciudad L, Ballestar M, Català-Moll F, Morante-Palacios O, Garcia-Gomez A, Celis R, Humby F et al : Inflammatory cytokines shape a changing DNA methylome in monocytes mirroring disease activity in rheumatoid arthritis. Annals of the Rheumatic Diseases 2019, 78:1505–1516. Ding N, Maiuri AR, O’Hagan HM: The emerging role of epigenetic modifiers in repair of DNA damage associated with chronic inflammatory diseases. Mutation Research/Reviews in Mutation Research 2019, 780:69–81. Cappellini MD, Comin-Colet J, de Francisco A, Dignass A, Doehner W, Lam CS, Macdougall IC, Rogler G, Camaschella C, Kadir R et al : Iron deficiency across chronic inflammatory conditions: International expert opinion on definition, diagnosis, and management. American Journal of Hematology 2017, 92(10):1068–1078. Wang Z, Liu Y, Zhang S, Yuan Y, Chen S, Li W, Zuo M, Xiang Y, Li T, Yang W et al : Effects of iron homeostasis on epigenetic age acceleration: a two-sample Mendelian randomization study. Clinical Epigenetics 2023, 15(1):159. Safiri S, Kolahi AA, Hoy D, Smith E, Bettampadi D, Mansournia MA, Almasi-Hashiani A, Ashrafi-Asgarabad A, Moradi-Lakeh M, Qorbani M et al : Global, regional and national burden of rheumatoid arthritis 1990–2017: a systematic analysis of the Global Burden of Disease study 2017. Annals of the Rheumatic Diseases 2019, 78(11):1463. Additional Declarations No competing interests reported. Supplementary Files Supplementarymatrial.docx Cite Share Download PDF Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Clinical Epigenetics → Version 1 posted Editorial decision: Revision requested 19 May, 2025 Reviews received at journal 14 Apr, 2025 Reviewers agreed at journal 29 Mar, 2025 Reviewers agreed at journal 24 Mar, 2025 Reviewers invited by journal 24 Mar, 2025 Editor assigned by journal 22 Mar, 2025 Submission checks completed at journal 21 Mar, 2025 First submitted to journal 12 Mar, 2025 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-6211246","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":435831535,"identity":"7ea967e9-841f-4d1a-a25c-135340757787","order_by":0,"name":"Yuhang Ou","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yuhang","middleName":"","lastName":"Ou","suffix":""},{"id":435831536,"identity":"63f4b26c-d496-42de-b3e1-200f53629769","order_by":1,"name":"Zhihao Wang","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Zhihao","middleName":"","lastName":"Wang","suffix":""},{"id":435831537,"identity":"d085d1fb-003a-4904-befb-5d946a4e9304","order_by":2,"name":"Yunbo Yuan","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yunbo","middleName":"","lastName":"Yuan","suffix":""},{"id":435831538,"identity":"cc796a31-7ea6-41ab-8600-1dd51c71dc67","order_by":3,"name":"Yuze He","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yuze","middleName":"","lastName":"He","suffix":""},{"id":435831539,"identity":"9a34893b-6119-43e5-b411-7167c52ef78b","order_by":4,"name":"Wenhao Li","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Wenhao","middleName":"","lastName":"Li","suffix":""},{"id":435831540,"identity":"a8a7e937-27f0-4680-83bc-823a75570624","order_by":5,"name":"Hao Ren","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Ren","suffix":""},{"id":435831541,"identity":"88acd1a5-aa3d-45d9-bcaa-5c5b619ef75a","order_by":6,"name":"Junhong Li","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Junhong","middleName":"","lastName":"Li","suffix":""},{"id":435831542,"identity":"79c19ab8-2032-4c7d-a3fd-7e1bca33d1a7","order_by":7,"name":"Siliang Chen","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Siliang","middleName":"","lastName":"Chen","suffix":""},{"id":435831543,"identity":"937e1283-0bd6-452d-80db-eef41ee793c5","order_by":8,"name":"Yanhui Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYBACPmYGBmaGCghHgigtbGAtZ0jSAsTMjG0kaWHnMZMunFcnb3CA+eBtHga7PCIcBtQyc9thww0H2JKteRiSi4nTwrvtQILBASCDh+FAYgNxWubUAbXwfyNFSwMzyBY2YrWwFVvzHDtsOPMwm7HlHINkwlr4+Q9vvM1TUyfPd7z54Y03FXaEtQABCyQ6mEGEARHqQWo/EKduFIyCUTAKRiwAAPM0LQ+DkmCQAAAAAElFTkSuQmCC","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Yanhui","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-03-12 10:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6211246/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6211246/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13148-025-01919-8","type":"published","date":"2025-07-02T15:57:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79825768,"identity":"f42e1dcb-f437-42bc-942a-d75a17139852","added_by":"auto","created_at":"2025-04-03 09:27:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4849077,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of inclusion and exclusion strategy\u003c/p\u003e\n\u003cp\u003eNHANES = National Health and Nutrition Examination Survey. RA = rheumatoid arthritis.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6211246/v1/bebec938b5dca53f17a9bd9d.png"},{"id":79826209,"identity":"db431efb-9a58-4500-bca4-69845b19852c","added_by":"auto","created_at":"2025-04-03 09:35:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1275500,"visible":true,"origin":"","legend":"\u003cp\u003eDose-response association between epigenetic age and risk of RA\u003c/p\u003e\n\u003cp\u003e(A) GrimAge2, (B) GrimAge. Shaded areas represent 95% CIs. \u003cem\u003eP\u003c/em\u003e-non-linear\u0026gt;0.05 suggests no significant nonlinearity and indicates a linear trend.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6211246/v1/0e4ba3d66c5e9df7b57fe2db.png"},{"id":79825771,"identity":"72cbcad8-7338-4ed3-aacc-372c44871b4e","added_by":"auto","created_at":"2025-04-03 09:27:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4767460,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of epigenetic age acceleration with RA mortality\u003c/p\u003e\n\u003cp\u003eModel was adjusted for age and sex. If the horizontal 95% CI lines cross the vertical reference line, then results are judged to be statistically non-significant.\u003c/p\u003e\n\u003cp\u003eHR=hazard ratio. HorvathAgeAccel=HorvathAge acceleration. HannumAgeAccel=HannumAge acceleration. PhenoAgeAccel=PhenoAge acceleration. GrimAgeAccel=GrimAge acceleration. GrimAge2Accel=GrimAge2 acceleration.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6211246/v1/1734c215a518c82057cdfa2e.png"},{"id":79825767,"identity":"8b230c96-2d81-4df5-82e4-248acb28f82b","added_by":"auto","created_at":"2025-04-03 09:27:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2958002,"visible":true,"origin":"","legend":"\u003cp\u003eNomograms for survival prediction of RA patients with GrimAge2Accel\u003c/p\u003e\n\u003cp\u003eEach variable axis represents a predictor factor, with the coordinates above the axis indicating the values of the variable. The corresponding point value is used to quantify the effect of that variable on the prediction outcome. The total points are obtained by summing the point values of each variable, with higher total points representing a lower survival probability for RA patients (1-year, 10-year, and 20-year survival).\u003c/p\u003e\n\u003cp\u003eGrimAge2Accel=GrimAge2 acceleration. BMI\u003cstrong\u003e=\u003c/strong\u003e body mass index. PIR=poverty income ratio.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6211246/v1/feb8bf9a0f2e22877629bee1.png"},{"id":79825766,"identity":"f930269e-0c78-4124-ad65-9d8eded756d8","added_by":"auto","created_at":"2025-04-03 09:27:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2898408,"visible":true,"origin":"","legend":"\u003cp\u003ePerformance of GrimAge2Accel in predicting survival probability of RA patients\u003c/p\u003e\n\u003cp\u003e(A-C) Calibration curves of nomograms for the three models. (D-F) Comparison of the ROC curves of the three models for predicting survival probability of RA patients.\u003c/p\u003e\n\u003cp\u003eModel 1, adjusted for age and sex. Model 2, adjusted for age, race, smoking status, education level, and diabetes. Model 3, adjusted for age, sex, race, smoking status, alcohol intake, BMI, vigorous activity, moderate activity, PIR, education level, diabetes, hypertension, and marital status.\u003c/p\u003e\n\u003cp\u003eGrimAge2Accel=GrimAge2 acceleration. ROC=receiver operating characteristic.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6211246/v1/81215a10f59710a27dcb553b.png"},{"id":86178957,"identity":"2000b23a-dcda-450b-b088-49360f0e66d3","added_by":"auto","created_at":"2025-07-07 16:12:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":17977498,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6211246/v1/9e2756d4-ed6f-4343-a25b-8940f207a685.pdf"},{"id":79825788,"identity":"c9928ca9-1a88-4b95-96af-8617b28a0215","added_by":"auto","created_at":"2025-04-03 09:27:55","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":109604066,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymatrial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6211246/v1/4404ef361369040b135d3a5e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Epigenetic Age Acceleration and Rheumatoid Arthritis: An NHANES-Based Analysis and Survival Prediction Models","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRheumatoid arthritis (RA) is a complex autoimmune disease characterized by chronic joint inflammation and accompanied by multi-systemic damage [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Globally, approximately one out of every 200 people is affected by RA, with 2\u0026ndash;3 times higher incidence in females than in males [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The etiology and pathogenesis of RA are not yet fully understood, despite genetics, environmental factors, and immune dysregulation have been found to potentially play roles in disease progression [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Currently, genetics is recognized as one of the most prominent risk factors for RA, encompassing both genetic and epigenetic risks [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The most direct evidence of genetic risk for RA is that first-degree relatives of RA patients have 2 to 5-fold higher risk of disease compared to whom of non-RA controls [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Epigenetic factor could also contribute to RA risk. Compared to monogenic diseases, the concordance rate for RA prevalence among monozygotic twins is relatively low, at approximately 15% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and prevalence-discordant monozygotic twins of RA exhibit different DNA methylation (DNAm) patterns [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Besides, changes in histone modifications, X-chromosome activity, and non-coding RNA expression patterns were all observed in RA patients [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These suggest that epigenetics may be a key regulatory mechanism in the pathological processes of RA, providing new insights for exploring therapeutic and preventive strategies.\u003c/p\u003e \u003cp\u003eAlthough RA can occur at any age, it typically peaks in older age, suggesting aging as one of the hallmarks of RA [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In the aspect of biology, aging does not solely refer to the increase in chronological age, more precisely, it refers to age-dependent functional decline [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], a complex biological process characterized by the gradual decline of bodily functions and an increased risk of multi-system disease [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Intriguingly, aging-associated phenotypes, including immunosenescence, cellular senescence, and telomere shortening, are all potential mechanisms involved in the pathogenesis of RA [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Similar phenomenon of immune system aging can be observed in both aging individuals and RA patients, particularly the enhanced innate immune response and the diminished adaptive immune response [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], which suggests that aging may contribute to the initiation and progression of RA through alterations in the immune system. In addition, age-related comorbidities can also affect the course of autoimmune diseases [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Depression is the most common comorbidity in RA [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and a cohort study has demonstrated a significant elevation in RA risk with depression [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Overall, how aging might induce RA was still not completely clear.\u003c/p\u003e \u003cp\u003eWhile chronological aging is uniform across individuals, the speed of functional decline exhibits considerable heterogeneity [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. To better define aging-related biological decline, several biomarkers have been used, including DNAm, telomere length, transcriptomics, proteomics, metabolomics and composite biomarker panels [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. As one of the most promising predictors, DNAm was able to reflect both genetic and environmental characteristics of individuals [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. As an epigenetic modification, DNAm typically occurs in DNA regions rich in cytosine-phosphate-guanine (CpG) dinucleotides, where the cytosines become covalently linked to methyl groups, leading to methylation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Changes in certain DNAm sites correlate linearly with age, for instance, CpG sites (CpGs) in promoter regions often undergo hypermethylation during aging, while other CpGs shift to a hypomethylated state [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This kind of biological aging based on changes in DNAm patterns, known as epigenetic aging, is considered a more accurate reflection of an organism's biological age and health status than chronological age[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Existing studies have developed methods to measure epigenetic ageing, referred to as epigenetic clocks [\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], utilizing DNAm data to estimate telomere length and integrating other aging markers through the inclusion of clinical, lifestyle, and immune biomarkers, the interpretability of which has been supported by reliable findings [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. As for epigenetic clocks and RA risk, previous studies have reported that RA patients exhibit higher epigenetic age acceleration compared to non-RA controls [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Additionally, significant changes in DNAm patterns have been observed in specific tissues, such as synovium and immune cells [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], suggesting that epigenetic aging may play a key role in the onset and progression of RA.\u003c/p\u003e \u003cp\u003eTo explore the role of epigenetic aging in RA, particularly its relationship with RA prognosis, in this cross-sectional and prospective study, we utilized data from the National Health and Nutrition Examination Survey (NHANES), analyzing the association of five well-validated epigenetic clocks with both prevalence and mortality risk of RA and established prediction models for short-, mid-, and long-term survival probabilities accordingly. Our findings could shed light on the value of epigenetic clocks in the prognosis and survival prediction of RA.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy design and population\u003c/p\u003e\n\u003cp\u003eIn this cross-sectional and prospective study, we used data from the NHANES to assess their survival prediction value of five measures of epigenetic age acceleration on participants with RA. Protocols of the NHANES have been approved by The National Center for Health Statistics ethics review board. Subjects from the NHANES 1999 to 2002 were enrolled, as their epigenetic biomarkers were available. Our inclusion and exclusion strategy were displayed in \u003cstrong\u003eFigure 1\u003c/strong\u003e. In the DNAm dataset, a total of 4,449 participants aged over 50 years were documented, among which 2,532 had data on epigenetic biomarkers, and none of them were excluded due to ineligible mortality follow-up. Ultimately, a total of 2,248 participants without RA and 284 RA patients without missing information were analyzed in this study.\u003c/p\u003e\n\u003cp\u003eMortality outcome\u003c/p\u003e\n\u003cp\u003eThe mortality of subjects was from the National Center for Health Statistics. Only participants with eligible follow-up were used for survival analysis. Eligible participants were coded as 0 (assumed alive) and 1 (assumed deceased). The follow-up time was measured as month from the date on the NHANES interview or mobile examination center, in which the larger number was applied as the survival time. Complete description of mortality data methodology was available at: https://www.cdc.gov/nchs/data-linkage/mortality-public.htm.\u003c/p\u003e\n\u003cp\u003eEpigenetic clocks and epigenetic age acceleration\u003c/p\u003e\n\u003cp\u003eWe used five commonly applied and well-validated epigenetic age acceleration measures, referred to as epigenetic clocks, to assess epigenetic aging in all participants: Horvath\u0026rsquo;s epigenetic clock, Hannum\u0026rsquo;s epigenetic clock, PhenoAge, GrimAge, and GrimAge version 2 (GrimAge2). Horvath\u0026rsquo;s clock and Hannum\u0026rsquo;s clock are known as the first generation of epigenetic clocks. Horvath\u0026rsquo;s clock is the first multi-tissue age estimator, based on DNAm data from multiple tissues or cell types at different stages of life, from which 353 age-associated CpGs are selected to construct a predictive model of epigenetic age [19]. Hannum\u0026rsquo;s clock, constructed using DNAm data from 71 age-related CpGs in adult whole blood samples [20], demonstrates higher accuracy in estimating chronological age compared to Horvath\u0026rsquo;s clock when applied to adult blood samples, but shows considerable bias when applied to non-blood tissues or in children [25]. PhenoAge and GrimAge are considered second-generation epigenetic clocks. In addition to using DNAm data, they incorporate various biomarkers and clinical features, focusing more on predicting biological age and the incidence of adverse outcomes [15]. PhenoAge integrates chronological age and nine mortality-associated biomarkers as composite predictor variables, employing regression analysis on DNAm data from 513 CpGs in whole blood samples to predict \u0026quot;phenotypic age\u0026quot; rather than chronological age [21]. GrimAge, constructed based on a series of plasma protein biomarkers, smoking pack-years and 1,030 CpGs, regresses time-to-death on the biomarkers, whose focus on lifestyle factors and disease outcomes makes it a better predictor of individual lifespan and mortality risk [22, 26]. The biological age of an individual can be comprehensively assessed from the perspective of DNAm using the epigenetic age calculated by Horvath\u0026rsquo;s clock (HorvathAge) and Hannum\u0026rsquo;s clock (HannumAge), as well as PhenoAge and GrimAge [26-29]. Building on GrimAge, the new version, GrimAge2, adopts another two DNAm-based log transformed estimators of plasma proteins and outperforms GrimAge in predicting mortality across multiple racial population and the onset of various age-related diseases [30]. All five measures were directly obtained from the DNAm dataset, with description of sample selection, laboratory test, data pre-processing and biomarker generation available from https://wwwn.cdc.gov/nchs/nhanes/dnam/. Epigenetic age acceleration was defined as the residual value computed by a certain epigenetic age relative to chronological age, where a positive result of age acceleration indicated accelerated aging (age acceleration greater than 0), the converse would refer to non-accelerated aging (age acceleration less than or equal to 0).\u003c/p\u003e\n\u003cp\u003eAssessment of RA\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe definition of RA was based on the Medical Conditions Questionnaire of the NHANES that two arthritis-related questions were included: \u0026ldquo;Has a doctor or other health professional ever told you that you have arthritis?\u0026rdquo; Those who answered affirmatively were then asked further onwards, \u0026ldquo;Which type of arthritis was it?\u0026rdquo; The participants who answered \u0026ldquo;rheumatoid arthritis\u0026rdquo; were identified as RA patients [31].\u003c/p\u003e\n\u003cp\u003eCovariates\u003c/p\u003e\n\u003cp\u003eWe controlled a series of factors that potentially affected the RA outcome as covariates in the Cox proportional hazards regression model and logistic regression models: age (continuous), sex (female, male), race (non-Hispanic black, non-Hispanic white, other Hispanic, other race - including multi-racial), smoking status (current, former, never), alcohol intake (current, former, never),\u0026nbsp;body mass index (BMI,\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e, continuous), vigorous activity (yes, no), moderate activity (yes, no),\u0026nbsp;poverty-income ratio (PIR,\u0026nbsp;continuous),\u0026nbsp;education level (9-11th grade - including 12th grade with no diploma, high school grade/GED or equivalent, some college or AA degree, college graduate or above), diabetes (yes, no), hypertension (yes, no), marital status (never married, separated, divorced, widowed).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u003c/p\u003e\n\u003cp\u003eR version 4.4.0 (https://www.r-project.org/) was used for statistical analysis and visualization of results. In data cleaning, any record of \u0026ldquo;Refuse to answer\u0026rdquo; and \u0026ldquo;Don\u0026apos;t know\u0026rdquo; were labeled as missing value. In descriptive statistics, continuous variables were described as mean \u0026plusmn; standard deviation (SD) and compared by t test or one-way ANOVA test, while categorical variables as frequency or percentage and its difference tested using chi-square test. Pearson correlation analysis was used to calculate the correlation coefficient (r), its confidence interval (CI) and \u003cem\u003ep\u003c/em\u003e value were given by Fisher\u0026apos;s Z transform. Restricted cubic splines (RCS) were used to examine the potential nonlinear dose-response relationship between epigenetic age and RA risk. Cox proportional hazards regression model and logistic regression models were used for survival analysis and prediction, with hazard ratio (HR) and 95% CI used to describe the mortality risk, analysis of receiver operating characteristic (ROC) curves was performed and the area under the curve (AUC) was utilized to evaluate the predictive efficiency of survival probability of 1, 10, and 20 years. R package \u0026ldquo;rms\u0026rdquo; version 6.8.1 (https://hbiostat.org/r/rms/) and \u0026ldquo;survival\u0026rdquo; version 3.5.8 (https://github.com/therneau/survival) were applied for survival analysis and prediction. Two-paired P value of 0.05 was set as threshold of statistically significance.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eCharacteristics of Study Participants\u003c/h2\u003e\n \u003cp\u003eAt baseline of this study, 284 of the 2,532 participants have reported being diagnosed with RA. Compared to participants without RA, RA patients were older (non-RA: 65.95 vs RA: 67.57, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), and had a higher proportion of female individuals (59.5% vs 48.0%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and more likely to have a higher BMI, a lower PIR, and a lower education level. There were more deceased individuals (52.7% vs 62.0%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) in RA patients than participants without RA, potentially related to the difference in overall follow-up time between the two groups (169.67 months vs 160.08 months, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033). The baseline characteristics of all participants are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics of participants by rheumatoid arthritis status.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-RA (n\u0026thinsp;=\u0026thinsp;2,248)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRA (n\u0026thinsp;=\u0026thinsp;284)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSex\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,078 (48.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e169 (59.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,170 (52.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115 (40.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, years\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.95 (10.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.57 (9.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e645 (28.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76 (26.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e447 (19.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91 (32.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e931 (41.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96 (33.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Race - Including Multi-Racial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76 (3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e334 (14.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (19.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e875 (39.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95 (33.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,034 (46.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134 (47.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol intake\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,366 (63.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140 (53.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e412 (19.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61 (23.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e366 (17.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62 (23.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI, kg/m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2 b\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.47 (5.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.37 (6.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eVigorous activity\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,839 (81.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e236 (83.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e407 (18.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 (16.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate activity\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,416 (63.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e204 (71.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e830 (37.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80 (28.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIR\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.64 (1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.12 (1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than 9th Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e592 (26.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102 (35.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9-11th Grade (Including 12th grade with no diploma)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e400 (17.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70 (24.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh School Grad/ GED or Equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e458 (20.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58 (20.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSome College or AA degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e431 (19.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (12.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollege Graduate or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e363 (16.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (7.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,849 (82.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e228 (80.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e398 (17.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55 (19.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,248 (58.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160 (58.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e903 (42.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112 (41.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried/ Living with partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,389 (64.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e158 (59.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever married/ Separated/ Divorced/ Widowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e764 (35.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109 (40.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality status\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFalse/ Survived\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,063 (47.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108 (38.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrue/ Deceased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,185 (52.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176 (62.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOS, months\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e169.67 (71.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160.08 (74.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.033\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Statistics were shown as N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Statistics were shown as mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003ePIR\u0026thinsp;=\u0026thinsp;poverty income ratio. RA\u0026thinsp;=\u0026thinsp;rheumatoid arthritis.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eAssociation of epigenetic age and epigenetic age acceleration with risk of RA\u003c/h2\u003e\n \u003cp\u003eTo thoroughly test the association of epigenetic age and epigenetic age acceleration with the risk of RA, we used the five different age measures mentioned above to estimate the epigenetic age of RA patients and non-RA participants, and calculated the corresponding age accelerations, including HorvathAge acceleration (HorvathAgeAccel), HannumAge acceleration (HannumAgeAccel), PhenoAge acceleration (PhenoAgeAccel), GrimAge acceleration (GrimAgeAccel), and GrimAge2 acceleration (GrimAge2Accel), five of those ten indicators were significantly associated with RA (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). We identified a negative association between HannumAgeAccel and RA that RA patients exhibited a decreased rate of aging compared with non-RA cases by 0.84 years (non-RA: 1.05 vs RA: 0.21, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032). In contrast, HorvathAge, PhenoAge, GrimAge, and GrimAge2 positively associated with RA. RA patients showed an increase rate of aging compared with non-RA participants by 1.35 years for HorvathAge (non-RA: 66.75 vs RA: 68.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034), 1.93 years for PhenoAge (55.58 vs 57.51, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), 1.75 years for GrimAge (66.19 vs 67.94, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), and 2.02 years for GrimAge2 (71.98 vs 74.00,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. HorvathAgeAccel, HannumAge, PhenoAgeAccel, GrimAgeAccel, and GrimAge2Accel did not demonstrate statistically significant association with RA prevalence.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociation of epigenetic age and epigenetic age acceleration with risk of RA.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEpigenetic aging marker\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-RA (n\u0026thinsp;=\u0026thinsp;2,248)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRA (n\u0026thinsp;=\u0026thinsp;284)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta; Value (RA - Non-RA)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHorvathAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.75 (9.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.01 (8.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.034\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHorvathAgeAccel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79 (6.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44 (5.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHannumAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.00 (9.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.78 (9.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHannumAgeAccel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05 (6.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21 (5.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.032\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhenoAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.58 (11.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.51 (10.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhenoAgeAccel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10.37 (7.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10.06 (7.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrimAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.19 (8.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.94 (8.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrimAgeAccel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23 (5.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37 (5.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrimAge2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.98 (8.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.00 (8.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrimAge2Accel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.03 (6.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.43 (5.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Statistics were shown as mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eHorvathAgeAccel\u0026thinsp;=\u0026thinsp;HorvathAge acceleration. HannumAgeAccel\u0026thinsp;=\u0026thinsp;HannumAge acceleration. PhenoAgeAccel\u0026thinsp;=\u0026thinsp;PhenoAge acceleration. GrimAgeAccel\u0026thinsp;=\u0026thinsp;GrimAge acceleration. GrimAge2Accel\u0026thinsp;=\u0026thinsp;GrimAge2 acceleration\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\u003eWith comparatively stronger statistical significance, GrimAge2 and GrimAge were further selected for RCS analysis, which indicated that higher GrimAge2 (\u003cem\u003ep\u003c/em\u003e-non-linear\u0026thinsp;=\u0026thinsp;0.192, \u003cem\u003ep\u003c/em\u003e-overall\u0026thinsp;=\u0026thinsp;0.002) and higher GrimAge (\u003cem\u003ep\u003c/em\u003e-non-linear\u0026thinsp;=\u0026thinsp;0.154, \u003cem\u003ep\u003c/em\u003e-overall\u0026thinsp;=\u0026thinsp;0.008) were linearly associated with the risk of RA (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eCollectively, we found that the risk of RA is generally associated with epigenetic age, particularly showing a positive linear dose-response relationship with GrimAge2 and GrimAge in this part.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eAssociation between epigenetic age acceleration and RA mortality\u003c/h2\u003e\n \u003cp\u003eTo verify the association between epigenetic age acceleration and RA mortality, Cox proportional hazards regression model was used to assess the impact of five epigenetic aging indicators on RA outcome. As age and sex affect both epigenetic age acceleration [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e] and RA [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e], the association was adjusted by incorporating age and sex as covariates in the model. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, in the NHANES 1999 to 2002, HorvathAgeAccel, HannumAgeAccel, and PhenoAgeAccel had no statistically significant effect on RA outcome (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, GrimAgeAccel and GrimAge2Accel significantly increased the risk of mortality in participants with RA. For each one-year increase in GrimAgeAccel, the risk of RA mortality increased by 6.4% (HR 1.064 [95% CI 1.032\u0026ndash;1.098], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), whereas each one-year increase in GrimAge2Accel increased the risk by 7.5% (1.075 [1.043\u0026ndash;1.107], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Therefore, GrimAgeAccel and GrimAge2Accel were selected to construct the predictive models for RA survival in the next step.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eSurvival prediction of RA mortality with epigenetic age acceleration\u003c/h2\u003e\n \u003cp\u003eTo assess the predictive efficiency of epigenetic age acceleration for RA mortality, firstly, the multivariable Cox regression model was conducted to evaluate the independent effects of each covariate on the survival outcome of participants with RA. As detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, in the NHANES 1999 to 2002, age, race, smoking status, education level, and diabetes were identified as risk factors independently associated with RA outcome and incorporated into one of the models of multivariable logistic regression analysis.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u0026nbsp;Association of covariates with RA mortality.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCovariate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.094 (1.0665\u0026ndash;1.121)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.380 (0.8509\u0026ndash;2.238)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.544 (0.8529\u0026ndash;2.795)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.290 (1.2546\u0026ndash;4.180)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.131 (0.0171\u0026ndash;1.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Race - Including Multi-Racial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.122 (1.3464\u0026ndash;19.487)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.476 (0.2749\u0026ndash;0.823)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.355 (0.2001\u0026ndash;0.630)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol intake\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.994 (0.5884\u0026ndash;1.678)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.170 (0.6465\u0026ndash;2.119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.603\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.015 (0.9849\u0026ndash;1.045)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eVigorous activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.677 (0.3637\u0026ndash;1.259)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.832 (0.5189\u0026ndash;1.335)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.896 (0.7607\u0026ndash;1.055)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than 9th Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9-11th Grade (Including 12th grade with no diploma)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.553 (0.3110\u0026ndash;0.983)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.044\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh School Grad/ GED or Equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.713 (0.3877\u0026ndash;1.311)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSome College or AA degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.506 (0.2423\u0026ndash;1.057)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollege Graduate or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.756 (0.3005\u0026ndash;1.901)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.828 (1.0932\u0026ndash;3.057)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.264 (0.8196\u0026ndash;1.948)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried/ Living with partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever married/ Separated/ Divorced/ Widowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.208 (0.7793\u0026ndash;1.871)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eHR\u0026thinsp;=\u0026thinsp;hazard ratio. PIR\u0026thinsp;=\u0026thinsp;poverty income 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\u003eThree logistic regression models were subsequently established to analyze the predictive efficiency of GrimAge2Accel for survival of RA patients: Model 1 was adjusted for age and sex, Model 2 was adjusted for the independent risk factors of RA outcome as described above, and Model 3 was adjusted for all covariates included in this study. The cut-off points of survival time were set as 1 year, 10 years, and 20 years, representing short-term, medium-term, and long-term survival, respectively. Nomograms for survival prediction of RA patients were constructed by combining the RA prognosis-related factors from each model with their respective weight values (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), and their corresponding calibration curves were plotted, which showed good consistency between the predicted survival and the actual survival, indicating no significant deviation between the predictions and actual outcome (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA-C). As shown in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD-F, Model 2 was the most accurate in predicting the 1-year survival probability of RA (AUC 0.856 [95% CI 0.666\u0026ndash;1.046]), while Model 3 excelled in predicting the 10-year (0.871 [0.819\u0026ndash;0.923]) and 20-year (0.898 [0.839\u0026ndash;0.956]) survival probability of RA. In addition, we employed the same approach to evaluate the predictive performance of GrimAgeAccel, which showed similar results to GrimAge2Accel. Specifically, GrimAgeAccel adjusted for independent risk factors was the best model for predicting 1-year survival probability (AUC 0.844 [95% CI 0.652\u0026ndash;1.036]), and when adjusted for all covariates, it exhibited the highest AUCs for the 10-year and 20-year survival probabilities (0.864 [0.809\u0026ndash;0.918] and 0.889 [0.826\u0026ndash;0.952], respectively). Taken together, the GrimAge2Accel-based and GrimAgeAccel-based prediction models could effectively predict the survival of RA patients.\u003c/p\u003e\n \u003cp\u003eThe complete evaluation results of the three models derived from GrimAgeAccel are presented in \u003cstrong\u003eFigure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e and \u003cstrong\u003eFigure S2\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003eEach variable axis represents a predictor factor, with the coordinates above the axis indicating the values of the variable. The corresponding point value is used to quantify the effect of that variable on the prediction outcome. The total points are obtained by summing the point values of each variable, with higher total points representing a lower survival probability for RA patients (1-year, 10-year, and 20-year survival).\u003c/p\u003e\n \u003cp\u003eGrimAge2Accel\u0026thinsp;=\u0026thinsp;GrimAge2 acceleration. BMI\u0026thinsp;\u003cstrong\u003e=\u003c/strong\u003e\u0026thinsp;body mass index. PIR\u0026thinsp;=\u0026thinsp;poverty income ratio.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study explored the association of epigenetic clocks with RA and its mortality risk through cross-sectional and prospective analyses. We found that increased HorvathAge, PhenoAge, GrimAge, and GrimAge2 were significantly associated with RA. Studies by Chen Li et al. and Mukherjee et al. also found that the biological age or epigenetic age of RA patients was higher than that of healthy controls [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, it is noteworthy that among the participants included in this study, HannumAgeAccel showed a negative correlation with the risk of RA. The data for Hannum's clock is derived solely from CpGs in adult blood samples without other clinical biomarkers or lifestyle factors included in the training, thus the cross-tissue applicability of which is limited, resulting in bias when applied to non-blood tissues [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. When Mukherjee et al. applied Hannum\u0026rsquo;s clock to the RA cohort, they also did not observe a correlation between HannumAge and RA [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Although in the study by Kresovich et al., HannumAgeAccel was statistically significantly associated with an increased risk of breast cancer [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], the methylation markers carried by ctDNA in blood are tumor-specific and used for transmitting epigenetic information [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The association between HannumAge and RA should be further verified.\u003c/p\u003e \u003cp\u003eMore importantly, we found that GrimAge2Accel and GrimAgeAccel were significantly associated with poor prognosis in RA, consistent with previous studies showing their association with all-cause mortality in multiple cohorts [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. When different covariates were included, the predictive models based on GrimAge2Accel and GrimAgeAccel showed all AUCs greater than 0.8, suggesting that these models could effectively predict the short-, medium-, and long-term survival probabilities of RA patients. Overall, our results suggest that increased epigenetic age is associated with RA, and the acceleration of epigenetic age may help identify the risk of mortality in RA patients. The predictive models built on this basis can serve as reliable predictors of RA survival probability.\u003c/p\u003e \u003cp\u003eAs for the pathogenesis of RA, researches have gradually shifted from a focus on immune abnormalities to a more integrated, multi-layered approach, including Immunological abnormalities, specific inflammatory pathway, metabolic disorders, genetic susceptibility, and epigenetic regulation-a new area of research [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Early studies have shown that T cells and fibroblast-like synovial cells in RA patients\u0026rsquo; joint biopsy samples exhibit significant DNA hypomethylation [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Similar observations were made in the IL-6 promoter region of peripheral blood mononuclear cells [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], accompanied by low expression of DNA methyltransferase 1 (DNMT1) and high expression of ten-eleven translocation protein 1 (TET1), TET2 and TET3 that involved in demethylation [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. However, the promoter region of the CTLA-4 in regulatory T cells (Treg) underwent hypermethylation [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. These inconsistent methylation changes are, to some extent, similar to the differential methylation patterns observed in aging [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This phenomenon may occur because the inflammatory driving cells and effector cells mentioned above could lower methylation levels through the decreased expression of methyltransferases and the increased expression of demethylases, thereby upregulating the expression of inflammation-related genes and promoting the chronic inflammatory of RA [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Under chronic inflammation, abnormal immune responses may impair DNA damage repair mechanisms, contributing to altered DNAm patterns [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Additionally, chronic inflammation is also responsible for iron buildup in the liver [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], which has been reported to be associated with epigenetic age acceleration [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In the abnormal methylation states, high methylation of NF-AT binding site in the CTLA-4 gene promoter of Treg cells results in reduced CTLA-4 expression, and, consequently, Treg cells cannot induce the expression and activation of indoleamine 2,3-dioxygenase, a tryptophan-degrading enzyme, and the kynurenine pathway remains inactive [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], in which manner, dysfunctional Treg cells fail to maintain immune balance, thus exacerbating systemic chronic inflammation. Thus, it is possible that methylation erasure of specific DNA sites in inflammation-related cells trigger the onset of chronic inflammation, which might promote the progression of RA, and induce abnormal epigenetic modifications. Methylation patterns alteration in certain pathway leads to immune dysregulation and finally results in unpleasant prognosis, which could be validated in further studies.\u003c/p\u003e \u003cp\u003eIn recent years, with the increasing life expectancy, the number of RA cases, age-standardized prevalence, and the disability-adjusted life years have been steadily rising worldwide, posing a significant disease burden [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. As alterations in DNAm patterns have been confirmed to play a potential role in the onset of RA, greater attention should be paid to the value of DNAm in predicting and intervening in RA prognosis, and, the strength of this study lies in its comprehensive analysis of RA and its poor prognosis with multiple epigenetic clocks, leading to the development of reliable RA survival probability prediction models.\u003c/p\u003e \u003cp\u003eThe main limitation of this study is that, although we have demonstrated the association of epigenetic aging with RA and its outcome, our observational study cannot establish specific causal relationship, or the mechanisms involved. However, the relatively small sample size limits the extrapolation to other populations. To overcome this limitation, future studies should consider expanding the sample size. The predictive models established in this study also need to be validated for robustness in other cohorts. In addition, although we have considered possible covariates in advance, we still cannot exclude the potential impact of other unknown factors on the statistical results.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the prediction models based on GrimAge2Accel and GrimAgeAccel could effectively predict the survival of RA patients, suggesting that epigenetic aging may play a promotive role in the onset and progression of RA. Identifying changes in biological markers associated with epigenetic aging may provide potential guidance for RA mortality prediction and prevention. Further researches are still needed to elucidate the underlying mechanisms and explore broader clinical implications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData was available from NHANES website via www.cdc.gov/nchs/nhanes/index.htm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur sincere thanks go to all participants and researchers in collecting and building NHANES, and developers and supporters of relevant software and packages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was fully supported by the Sichuan Science and Technology Program (2023YFQ0002 to Yanhui Liu).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZhihao Wang and Yanhui Liu designed the study.\u0026nbsp;Yuhang Ou,\u0026nbsp;Zhihao Wang, Yunbo Yuan, Yuze He, Wenhao Li, Hao Ren, Junhong Li, and Siliang Chen collected, analyzed and interpreted the data. Yuhang Ou and Zhihao Wang drafted the initial manuscript. All authors revised the manuscript. All authors reviewed the final manuscript and approved the submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the National Center for Health Statistics ethics review board. All human subjects provided written informed consent when participating.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSmith MH, Berman JR: What Is Rheumatoid Arthritis? JAMA 2022, 327(12):1194\u0026ndash;1194.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLittlejohn EA, Monrad SU: Early Diagnosis and Treatment of Rheumatoid Arthritis. Primary Care: Clinics in Office Practice 2018, 45(2):237\u0026ndash;255.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGravallese Ellen M, Firestein Gary S: Rheumatoid Arthritis \u0026mdash; Common Origins, Divergent Mechanisms. New England Journal of Medicine 2023, 388(6):529\u0026ndash;542.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eViatte S, Plant D, Han B, Fu B, Yarwood A, Thomson W, Symmons DPM, Worthington J, Young A, Hyrich KL \u003cem\u003eet al\u003c/em\u003e: Association of HLA-DRB1 Haplotypes With Rheumatoid Arthritis Severity, Mortality, and Treatment Response. JAMA 2015, 313(16):1645\u0026ndash;1656.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSILMAN AJ, MACGREGOR AJ, THOMSON W, HOLLIGAN S, CARTHY D, FARHAN A, OLLIER WER: TWIN CONCORDANCE RATES FOR RHEUMATOID ARTHRITIS: RESULTS FROM A NATIONWIDE STUDY. Rheumatology 1993, 32(10):903\u0026ndash;907.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSvendsen AJ, Gervin K, Lyle R, Christiansen L, Kyvik K, Junker P, Nielsen C, Houen G, Tan Q: Differentially Methylated DNA Regions in Monozygotic Twin Pairs Discordant for Rheumatoid Arthritis: An Epigenome-Wide Study. Frontiers in Immunology 2016, 7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarik RR, Bhatt LK: Emerging epigenetic targets in rheumatoid arthritis. Rheumatology International 2021, 41(12):2047\u0026ndash;2067.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoots AMH, Maier AB, Stinissen P, Masson P, Lories RJ, De Keyser F: The influence of ageing on the development and management of rheumatoid arthritis. Nature Reviews Rheumatology 2013, 9(10):604\u0026ndash;613.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeale K, Horvath S, Teschendorff A, Eynon N, Voisin S: Making sense of the ageing methylome. Nature Reviews Genetics 2022, 23(10):585\u0026ndash;605.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian YE, Cropley V, Maier AB, Lautenschlager NT, Breakspear M, Zalesky A: Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality. Nature Medicine 2023, 29(5):1221\u0026ndash;1231.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDegboe Y, Vastert SJ, Prakken BJ, McInnes IB: How does age determine the development of human immune-mediated arthritis? Nature Reviews Rheumatology 2022, 18(9):501\u0026ndash;512.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVallerand IA, Lewinson RT, Frolkis AD, Lowerison MW, Kaplan GG, Swain MG, Bulloch AGM, Patten SB, Barnabe C: Depression as a risk factor for the development of rheumatoid arthritis: a population-based cohort study. RMD Open 2018, 4(2):e000670.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePartridge L, Deelen J, Slagboom PE: Facing up to the global challenges of ageing. Nature 2018, 561(7721):45\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJylh\u0026auml;v\u0026auml; J, Pedersen NL, H\u0026auml;gg S: Biological Age Predictors. EBioMedicine 2017, 21:29\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuan R, Fu Q, Sun Y, Li Q: Epigenetic clock: A promising biomarker and practical tool in aging. Ageing Research Reviews 2022, 81:101743.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDay K, Waite LL, Thalacker-Mercer A, West A, Bamman MM, Brooks JD, Myers RM, Absher D: Differential DNA methylation with age displays both common and dynamic features across human tissues that are influenced by CpG landscape. Genome Biology 2013, 14(9):R102.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaul D, Kosinsky RL: Epigenetics of Aging and Aging-Associated Diseases. International Journal of Molecular Sciences 2021, 22(1):401.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorvath S, Raj K: DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nature Reviews Genetics 2018, 19(6):371\u0026ndash;384.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorvath S: DNA methylation age of human tissues and cell types. Genome Biology 2013, 14(10):3156.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, Klotzle B, Bibikova M, Fan JB, Gao Y \u003cem\u003eet al\u003c/em\u003e: Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell 2013, 49(2):359\u0026ndash;367.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, Hou L, Baccarelli AA, Stewart JD, Li Y \u003cem\u003eet al\u003c/em\u003e: An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY) 2018, 10(4):573\u0026ndash;591.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu AT, Quach A, Wilson JG, Reiner AP, Aviv A, Raj K, Hou L, Baccarelli AA, Li Y, Stewart JD \u003cem\u003eet al\u003c/em\u003e: DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging (Albany NY) 2019, 11(2):303\u0026ndash;327.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMukherjee N, Harrison TC: Epigenetic Aging and Rheumatoid Arthritis. The Journals of Gerontology: Series A 2023, 79(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNakano K, Whitaker J, Boyle D, Wang W, Firestein G: DNA methylome signature in rheumatoid arthritis. Annals of the Rheumatic Diseases 2012, 72:110\u0026ndash;117.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorvath S, Oshima J, Martin GM, Lu AT, Quach A, Cohen H, Felton S, Matsuyama M, Lowe D, Kabacik S \u003cem\u003eet al\u003c/em\u003e: Epigenetic clock for skin and blood cells applied to Hutchinson Gilford Progeria Syndrome and ex vivo studies. Aging (Albany NY) 2018, 10(7):1758\u0026ndash;1775.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCrory C, Fiorito G, Hernandez B, Polidoro S, O\u0026rsquo;Halloran AM, Hever A, Ni Cheallaigh C, Lu AT, Horvath S, Vineis P \u003cem\u003eet al\u003c/em\u003e: GrimAge Outperforms Other Epigenetic Clocks in the Prediction of Age-Related Clinical Phenotypes and All-Cause Mortality. The Journals of Gerontology: Series A 2020, 76(5):741\u0026ndash;749.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHillary RF, Stevenson AJ, McCartney DL, Campbell A, Walker RM, Howard DM, Ritchie CW, Horvath S, Hayward C, McIntosh AM \u003cem\u003eet al\u003c/em\u003e: Epigenetic measures of ageing predict the prevalence and incidence of leading causes of death and disease burden. Clin Epigenetics 2020, 12(1):115.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYusipov I, Kalyakulina A, Trukhanov A, Franceschi C, Ivanchenko M: Map of epigenetic age acceleration: A worldwide analysis. Ageing Research Reviews 2024, 100:102418.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Z, Kuo PL, Horvath S, Crimmins E, Ferrucci L, Levine M: A new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES IV: A cohort study. PLoS Med 2018, 15(12):e1002718.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu AT, Binder AM, Zhang J, Yan Q, Reiner AP, Cox SR, Corley J, Harris SE, Kuo PL, Moore AZ \u003cem\u003eet al\u003c/em\u003e: DNA methylation GrimAge version 2. Aging (Albany NY) 2022, 14(23):9484\u0026ndash;9549.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen L, Sun Q, Peng S, Tan T, Mei G, Chen H, Zhao Y, Yao P, Tang Y: Associations of blood and urinary heavy metals with rheumatoid arthritis risk among adults in NHANES, 1999\u0026ndash;2018. Chemosphere 2022, 289:133147.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrimmins EM, Thyagarajan B, Levine ME, Weir DR, Faul J: Associations of Age, Sex, Race/Ethnicity, and Education With 13 Epigenetic Clocks in a Nationally Representative U.S. Sample: The Health and Retirement Study. The Journals of Gerontology: Series A 2021, 76(6):1117\u0026ndash;1123.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePertsinidou E, Manivel VA, Westerlind H, Klareskog L, Alfredsson L, Mathsson-Alm L, Hansson M, Saevarsdottir S, Askling J, R\u0026ouml;nnelid J: Rheumatoid arthritis autoantibodies and their association with age and sex. Clin Exp Rheumatol 2021, 39(4):879\u0026ndash;882.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen L, Wu B, Mo L, Chen H, Zhao Y, Tan T, Chen L, Li Y, Yao P, Tang Y: Associations between biological ageing and the risk of, genetic susceptibility to, and life expectancy associated with rheumatoid arthritis: a secondary analysis of two observational studies. The Lancet Healthy Longevity 2024, 5(1):e45-e55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKresovich JK, Xu Z, O\u0026rsquo;Brien KM, Weinberg CR, Sandler DP, Taylor JA: Methylation-Based Biological Age and Breast Cancer Risk. JNCI: Journal of the National Cancer Institute 2019, 111(10):1051\u0026ndash;1058.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTerp SK, Stoico MP, Dybk\u0026aelig;r K, Pedersen IS: Early diagnosis of ovarian cancer based on methylation profiles in peripheral blood cell-free DNA: a systematic review. Clinical Epigenetics 2023, 15(1):24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHillary RF, Stevenson AJ, McCartney DL, Campbell A, Walker RM, Howard DM, Ritchie CW, Horvath S, Hayward C, McIntosh AM \u003cem\u003eet al\u003c/em\u003e: Epigenetic measures of ageing predict the prevalence and incidence of leading causes of death and disease burden. Clinical Epigenetics 2020, 12(1):115.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao Y, Zhang Y, Liu X: Rheumatoid arthritis: pathogenesis and therapeutic advances. MedComm (2020) 2024, 5(3):e509.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichardson B, Scheinbart L, Strahler J, Gross L, Hanash S, Johnson M: Evidence for impaired t cell dna methylation in systemic lupus erythematosus and rheumatoid arthritis. Arthritis \u0026amp; Rheumatism 1990, 33(11):1665\u0026ndash;1673.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarouzakis E, Gay RE, Michel BA, Gay S, Neidhart M: DNA hypomethylation in rheumatoid arthritis synovial fibroblasts. Arthritis \u0026amp; Rheumatism 2009, 60(12):3613\u0026ndash;3622.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNile CJ, Read RC, Akil M, Duff GW, Wilson AG: Methylation status of a single CpG site in the IL6 promoter is related to IL6 messenger RNA levels and rheumatoid arthritis. Arthritis \u0026amp; Rheumatism 2008, 58(9):2686\u0026ndash;2693.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Andres MC, Perez-Pampin E, Calaza M, Santaclara FJ, Ortea I, Gomez-Reino JJ, Gonzalez A: Assessment of global DNA methylation in peripheral blood cell subpopulations of early rheumatoid arthritis before and after methotrexate. Arthritis Research \u0026amp; Therapy 2015, 17(1):233.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCribbs AP, Kennedy A, Penn H, Read JE, Amjadi P, Green P, Syed K, Manka SW, Brennan FM, Gregory B \u003cem\u003eet al\u003c/em\u003e: Treg cell function in rheumatoid arthritis is compromised by ctla-4 promoter methylation resulting in a failure to activate the indoleamine 2,3-dioxygenase pathway. Arthritis Rheumatol 2014, 66(9):2344\u0026ndash;2354.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodr\u0026iacute;guez-Ubreva J, De La Calle-Fabregat C, Li T, Ciudad L, Ballestar M, Catal\u0026agrave;-Moll F, Morante-Palacios O, Garcia-Gomez A, Celis R, Humby F \u003cem\u003eet al\u003c/em\u003e: Inflammatory cytokines shape a changing DNA methylome in monocytes mirroring disease activity in rheumatoid arthritis. Annals of the Rheumatic Diseases 2019, 78:1505\u0026ndash;1516.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing N, Maiuri AR, O\u0026rsquo;Hagan HM: The emerging role of epigenetic modifiers in repair of DNA damage associated with chronic inflammatory diseases. Mutation Research/Reviews in Mutation Research 2019, 780:69\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCappellini MD, Comin-Colet J, de Francisco A, Dignass A, Doehner W, Lam CS, Macdougall IC, Rogler G, Camaschella C, Kadir R \u003cem\u003eet al\u003c/em\u003e: Iron deficiency across chronic inflammatory conditions: International expert opinion on definition, diagnosis, and management. American Journal of Hematology 2017, 92(10):1068\u0026ndash;1078.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z, Liu Y, Zhang S, Yuan Y, Chen S, Li W, Zuo M, Xiang Y, Li T, Yang W \u003cem\u003eet al\u003c/em\u003e: Effects of iron homeostasis on epigenetic age acceleration: a two-sample Mendelian randomization study. Clinical Epigenetics 2023, 15(1):159.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSafiri S, Kolahi AA, Hoy D, Smith E, Bettampadi D, Mansournia MA, Almasi-Hashiani A, Ashrafi-Asgarabad A, Moradi-Lakeh M, Qorbani M \u003cem\u003eet al\u003c/em\u003e: Global, regional and national burden of rheumatoid arthritis 1990\u0026ndash;2017: a systematic analysis of the Global Burden of Disease study 2017. Annals of the Rheumatic Diseases 2019, 78(11):1463.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"clinical-epigenetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clep","sideBox":"Learn more about [Clinical Epigenetics](http://clinicalepigeneticsjournal.biomedcentral.com/)","snPcode":"13148","submissionUrl":"https://submission.nature.com/new-submission/13148/3","title":"Clinical Epigenetics","twitterHandle":"@OAgenetics","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Epigenetic age acceleration, Rheumatoid arthritis, NHANES, Cross-sectional and prospective study","lastPublishedDoi":"10.21203/rs.3.rs-6211246/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6211246/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: Epigenetic aging has been confirmed to be associated with the pathogenesis of rheumatoid arthritis (RA), however, its role in the prognosis of RA remains unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: In this cross-sectional and prospective study, Epigenetic age and acceleration in participants of the National Health and Nutrition Examination Survey (NHANES) were calculated with Horvath’s clock, Hannum’s clock, PhenoAge, GrimAge, and GrimAge version 2 (GrimAge2). The association of epigenetic age and epigenetic age acceleration with the risk and mortality of RA was assessed with prediction models constructed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e:\u003cstrong\u003e \u003c/strong\u003eAccelerated epigenetic ageing increased the risk of RA mortality with hazard ratio of 1.075 (95% CI 1.043 - 1.107, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001) for GrimAge2 acceleration (GrimAge2Accel) and 1.064 (1.032 - 1.098, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001) for GrimAge acceleration (GrimAgeAccel). The GrimAge2Accel-based models, adjusted for three groups of covariates, excelled in predicting the 1-year, 10-year, and 20-year survival with area under curve of 0.856 (95% CI 0.666 - 1.046), 0.871 (0.819 - 0.923), and 0.898 (0.839 - 0.956), respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Epigenetic ageing may play a harmfully promotive role in the onset and progression of RA, and the GrimAge2Accel-based prediction models could effectively predict the survival of RA patients. Further research is needed to elucidate the underlying mechanisms and to explore the potential clinical implications.\u003c/p\u003e","manuscriptTitle":"Epigenetic Age Acceleration and Rheumatoid Arthritis: An NHANES-Based Analysis and Survival Prediction Models","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-03 09:27:43","doi":"10.21203/rs.3.rs-6211246/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-19T20:28:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-14T06:25:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8864016698624877530735555981562710718","date":"2025-03-30T03:02:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"102417092402946586720483198300885797985","date":"2025-03-24T16:50:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-24T16:34:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-22T08:02:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-21T04:43:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Clinical Epigenetics","date":"2025-03-12T10:29:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"clinical-epigenetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clep","sideBox":"Learn more about [Clinical Epigenetics](http://clinicalepigeneticsjournal.biomedcentral.com/)","snPcode":"13148","submissionUrl":"https://submission.nature.com/new-submission/13148/3","title":"Clinical Epigenetics","twitterHandle":"@OAgenetics","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3afc933b-1185-4dde-9bce-7be67e63b750","owner":[],"postedDate":"April 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T16:01:16+00:00","versionOfRecord":{"articleIdentity":"rs-6211246","link":"https://doi.org/10.1186/s13148-025-01919-8","journal":{"identity":"clinical-epigenetics","isVorOnly":false,"title":"Clinical Epigenetics"},"publishedOn":"2025-07-02 15:57:19","publishedOnDateReadable":"July 2nd, 2025"},"versionCreatedAt":"2025-04-03 09:27:43","video":"","vorDoi":"10.1186/s13148-025-01919-8","vorDoiUrl":"https://doi.org/10.1186/s13148-025-01919-8","workflowStages":[]},"version":"v1","identity":"rs-6211246","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6211246","identity":"rs-6211246","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}