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Trajectories of health status and their association with rheumatoid arthritis risk: insights from a national prospective cohort study

樱花视频 volume听25, Article听number:听1132 (2025) Cite this article

Abstract

Background

The association between trajectories of different health states and rheumatoid arthritis (RA) is unknown. Our cohort study aimed to investigate the impact of various trajectories (including depressive symptoms, physical activity and multimorbidity status) on subsequent RA risk.

Methods

A prospective cohort study was conducted using seven waves of national data from the Health and Retirement Study (HRS 2004鈥�2018) involving 9,795 US adults. A growth mixture model identified 6-year trajectories from 2004 to 2010, and participants were screened for RA by self-reported physician diagnosis in the subsequent four waves (2010鈥�2018). Cox proportional hazards model calculated hazard ratios (HR).

Results

Trajectories of depressive symptoms, physical activity, and multimorbidity status were all associated with the risk of RA. Specifically, keeping a low trajectories (HR鈥�=鈥�0.649, 95%CI鈥�=鈥�0.533鈥�0.790) or maintaining a moderate rating trajectories (HR鈥�=鈥�0.798, 95%CI鈥�=鈥�0.644鈥�0.988) for depressive reduced the risk of RA. For physical activity, both high and descending trajectories (HR鈥�=鈥�1.456, 95%CI鈥�=鈥�1.170鈥�1.812) and high and rising trajectories (HR鈥�=鈥�1.244, 95%CI鈥�=鈥�1.016鈥�1.522) increased the risk. High multimorbidity trajectories (HR鈥�=鈥�1.305, 95%CI鈥�=鈥�1.094鈥�1.556) and highest multimorbidity trajectories (HR鈥�=鈥�1.393, 95%CI鈥�=鈥�1.131鈥�1.715) increased the risk.

Conclusion

The results suggest that tracking trajectories of depressive symptoms, physical activity, and multiple disease states may be a potential and feasible screening method for identifying those at risk for RA.

Peer Review reports

Introduction

Rheumatoid Arthritis (RA) is a systemic polyarticular chronic autoimmune disease primarily affecting small joints such as those in the hands and feet. Its pathology is characterized by immune cell infiltration, synovial hyperplasia, abscess formation, and the destruction of articular cartilage and bone, leading to severe joint function impairment and significant quality of life reduction [1, 2]. RA not only impacts joints but can also cause systemic symptoms and complications like fatigue, fever, weight loss, cardiovascular disease, lung disease, and osteoporosis [3]. RA鈥檚 chronic and systemic nature significantly impacts daily life. According to 2020 statistics, approximately 17.6 million people worldwide suffer from RA [4]. The UK National Audit Office estimates that RA costs the UK 拢560 million annually in healthcare expenditures, excluding costs from sickness absence, work-related losses, and reduced productivity [5, 6].

Existing studies suggest that genetic and environmental factors play crucial roles in the development of RA [7]. Observational studies have shown that long-term factors such as depressive symptoms, physical inactivity, and multimorbidity are associated with an increased risk of RA. Studies have shown that the prevalence of depression in RA patients is 2鈥�3 times higher than in the general population [8]. Regular physical activity has been shown to significantly improve cardiovascular health, functional capacity, muscle strength, and overall quality of life in RA patients [9]. Another study indicated that reducing sedentary behavior significantly improved disease activity in RA patients [10]. Multimorbidity also complicates the management of RA and leads to a poorer prognosis. RA patients with four or more comorbidities had significantly higher fatigue scores than those with fewer comorbidities. For each additional comorbidity, the total fatigue score increased by 2.33 units [11]. Given that these factors may contribute to RA onset rather than merely coexisting with the disease, a longitudinal approach is essential to distinguish causality from association.

However, most studies have examined variables such as depressive symptoms, physical activity, and multimorbidity in cross-sectional studies. This assessment method often leads to difficulties in clarifying disease pathogenesis and causal associations because these symptoms frequently fluctuate during different stages of the disease. Single-point measurements may lead to false associations with other variables. Therefore, observation over a longer period is advantageous compared to single assessments [12]. Therefore, tracking long-term trajectories, as done in a study using data from the Chinese Longitudinal Study of Health and Retirement [13], provides a more comprehensive understanding of how these factors evolve and potentially influence RA onset.

Growth mixture modeling (GMM) is a statistical method that can reveal the impact of long-term health factors (such as depressive symptoms, physical inactivity, and multimorbidity) on the onset of RA [14]. Given the advantages of GMM in dynamically describing patients and the impact of depressive symptoms, physical, and fluctuations in multimorbidity on RA. This study aims to identify the characteristics of different RA patient groups and assess their risk of developing RA over time. This approach provides a basis for personalized treatment strategies, improves patients' quality of life, and reveals how depressive symptoms, physical inactivity, and multimorbidity influence RA development. Ultimately, GMM can evaluate the long-term effects of interventions and build personalized prediction models for more effective prevention and treatment.

Methods

Study design and subjects

We used cohort data from the Health and Retirement Study (HRS), a nationally representative longitudinal survey of adults aged 50 years and older in the US [15, 16]. The HRS collects data covering a wide range of information from demographics to physical and mental health. For more detailed information on the sample design and procedures, please see the cohort profile [17]. The reliability and validity of the HRS study were ensured by standardized and validated measures of depressive status, physical activity, and multimorbidity status. Our study met the reporting requirements of the Enhanced Guidelines for Reporting Observational Studies in Epidemiology.

The study population consisted of participants with undiagnosed RA. In the 2002 survey, the questionnaire did not address specific types of arthritis. Instead, data on depressive status, physical activity, and multimorbidity status were collected in the 2004, 2006, and 2010 surveys to determine their 6-year trajectories. Follow-up visits were conducted every 2 years until 2018 to confirm the presence of RA. The data from the three assessments were used to construct trajectory models for different health conditions. The flowchart illustrating participant selection is shown in Fig. 1.

Fig. 1
figure 1

Flow chart of the patient selection process

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At baseline, patients with a diagnosis of RA were excluded, and subjects who lacked sufficient information on depressive status, multimorbidity status, or physical activity were further excluded. For variables with minimal missing information, such as BMI, appropriate imputation methods, such as mean imputation, were used. Ultimately, 9,795 subjects were included in this cohort study.

Assessment of depressive symptoms, physical activity and multimorbidity status

Depressive symptoms were assessed using the Centers for Epidemiologic Studies Depression (CES-D) scale, which measures the frequency of feelings in the past week across eight dichotomous items, including 鈥渄epressed鈥�, 鈥渆verything is an effort鈥�, 鈥渉appy鈥�, 鈥渆njoyed life鈥�, 鈥渟ad鈥� and 鈥渃ould not get going鈥� [18, 19]. We reverse-coded the 鈥渉appy鈥� and 鈥渆njoyed life鈥� items and then summed all the items. The total score ranged from 0 to 8, with higher scores indicating more severe depressive symptoms.

Multimorbidity status was assessed by calculating a total score (0鈥�7) for seven self-reported physician-diagnosed conditions, including type 2 diabetes, stroke, hypertension, heart disease, chronic lung disease, mental illness, and cancer. At baseline, participants were asked, 鈥淗as your doctor ever told you that you have鈥�,鈥� and the conditions were reconfirmed at follow-up [20].

Physical activity levels were assessed using survey questions on the frequency of participation (almost never, 1鈥�3 times per month, once per week, more than once per week, daily) in activities of varying energy intensities (light, moderate, vigorous). We used the published Physical Activity Compendium, which provides a coding scheme for specific physical activities in terms of energy expenditure levels or metabolic equivalents (METs) [21]. This compendium converts responses to activity questions into "MET-equivalent activity points" to estimate energy expenditure [22]. The MET-equivalent activity points range from 0 to 17. For the five frequency levels mentioned, MET activity points range from 0 to 4 for mild activity (1-point interval between frequency levels), 0 to 10 for moderate activity (2.5-point interval between frequency levels), and 0 to 17 for vigorous activity (4.25-point interval between frequency levels). Indicators based on MET activity points have been shown to have good reliability and moderate validity compared to direct activity measures [23].

Evaluation of rheumatoid arthritis

In the self-reported doctor-diagnosed illness, participants were asked 鈥淒id your doctor tell you that you have arthritis?鈥� and if participants answered yes, they were further asked about the specific type of arthritis they had 鈥淲hat type of arthritis do you have?鈥� which determined whether the participant had RA.

Covariate

Covariates were assessed using information from the 2010 physical examination. The following indicators were included: age (<鈥�60, 60鈥�70, 71鈥�80,鈥�>鈥�80), sex (female, male), self-reported body mass index (BMI; underweight, normal, obese, overweight), education (less than high school, high school or partially tertiary, tertiary or above), marital status (married, unmarried), alcohol consumption (no, yes), and smoking (no, yes).

Statistical analysis

The distribution of participants was first described, and their data on depression status scores, multimorbidity status scores, and physical activity scores were computed. Model selection was then performed using GMM to determine the best-fitting model and the number of different trajectory categories. This process included unconditional model fitting and the calculation of model fit indices, including the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Adjusted Bayesian Information Criterion (aBIC), entropy, Lo-Mendell-Rubin (LMR), Adjusted Lo-Mendell-Rubin Likelihood Ratio test (aLMR) and Bootstrap Likelihood Ratio Test (BLRT) [24], to assess model quality and classification accuracy. In selecting the final model, we prioritized the lowest aLMR results, and an entropy value above 0.75 to ensure good classification quality. Additionally, we ensured that no trajectory group contained fewer than 5% of the participants, as a further criterion for model selection.

Subsequently, trajectory analyses were performed to sequentially add trajectory categories and compare model fit metrics, such as fitting linear functions, quadratic functions, etc., to better describe trends in the data. After the best model was identified, the parameters of the best-fit model were calculated to provide information about the characteristics and trends of the different trajectory categories. Finally, the probability of each subject belonging to different trajectory categories was obtained from the GMM model, providing insights into their potential growth trajectories in terms of depression status scores, multimorbidity status scores, and physical activity scores. This analytical approach helped to provide a comprehensive understanding of the patterns of change and characteristics of different populations on these scoring metrics.

To assess the risk of RA, the date of the third examination (i.e., 2010) was used as the time-zero point for the time-to-event analysis. Time to event (i.e., onset of RA) was defined as follows: participants were followed up from the start date of the survival analyses (2010) and then examined at the date of diagnosis of RA, the date of death, or the date of loss to follow-up. The date of loss to follow-up was the date the participant was last observed or contacted. After adherence to the proportional hazards model assumption was assessed by plotting smooth Schoenfeld residuals over time, no violations of the assumption were found. Hazard ratios (HRs) for RA were estimated using Cox proportional hazards models based on the specified trajectories. For all analyses, three models were fitted: model 1 was a univariable model; model 2 was adjusted for age, sex, education, marital status, self-reported body mass index, alcohol consumption, and smoking; and model 3 was a full model with all trajectories added. Descriptive analyses and Cox models were performed using R (4.3.2), and GMM models were estimated using Mplus 8. Statistical significance was set at P鈥�<鈥�0.05, and all P-values were two-sided.

Results

Baseline characteristics

In the 2018 study sample, there were a total of 9,795 participants, of which 8,755 (89.38%) did not have RA (Non-RA) and 1,040 (10.62%) were diagnosed with RA. Data on baseline characteristics showed statistically significant differences in age (P鈥�=鈥�0.002), BMI (P鈥�<鈥�0.001), education (P鈥�<鈥�0.001), and alcohol consumption (P鈥�<鈥�0.001), suggesting that these factors are strongly associated with the incidence of RA. However, the differences were not statistically significant (P鈥�>鈥�0.05) on gender (P鈥�=鈥�0.362), smoking (P鈥�=鈥�0.138) and marital status (P鈥�=鈥�0.291), suggesting that gender, marital status and smoking may not be the key influencing factors for the incidence of RA (Table 1).

Table 1 Baseline characteristics
Full size table

Trajectories of depressive symptoms, physical activity, and multimorbidity status

Interpretability and parsimony were also considered when determining the number of trajectory groups, leading to the selection of the 3-class, 5-class, and 4-class models as the most appropriate options (Supplementary Table 1). Of the 9,795 participants, a total of 5,980 were females (61.05%) and 3,815 were males (38.95%). Specifically, for depressive symptoms, the proportion of RA cases across the three trajectories (keep low, maintain a moderate rating, downward trend) are 9.25%, 13.02%, and 17.68%, respectively (Fig. 2A). For physical activity, the proportion of RA cases across the five trajectories (rise rapidly, keep low, high and descending, high and rising, decline rapidly) are 10.14%, 7.38%, 13.58%, 11.02%, and 9.84%, respectively (Fig. 2B). For multimorbidity status, the proportion of RA cases across the four trajectories (low to highest) are 8.34%, 9.92%, 13.05%, and 15.43%, respectively (Fig. 2C).

Fig. 2
figure 2

Trajectories of depressive symptoms, physical activity, and multimorbidity status over time (2004鈥�2010)

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Association of trajectories of depressive symptoms, physical activity, and multimorbidity status with risk of rheumatoid arthritis

In univariable models, it was found that keeping a low rating for depressive status (HR鈥�=鈥�0.498, 95%CI鈥�=鈥�0.415鈥�0.598) and maintaining a moderate rating trajectory (HR鈥�=鈥�0.699, 95%CI鈥�=鈥�0.567鈥�0.862) were associated with a lower risk of RA, compared with a downward trend trajectory for depressive status. Additionally, in the physical activity trajectory, individuals with a rise rapidly trajectory (HR鈥�=鈥�1.396, 95%CI鈥�=鈥�1.093鈥�1.783), those with a high and descending trajectory (HR鈥�=鈥�1.971, 95%CI鈥�=鈥�1.602鈥�2.425), and those with a high and rising trajectory (HR鈥�=鈥�1.495, 95%CI鈥�=鈥�1.228鈥�1.819) were found to be at a higher risk of RA. Using the lowest trajectory as the reference, high trajectories of multimorbidity status (HR鈥�=鈥�1.547, 95%CI鈥�=鈥�1.306鈥�1.832) and highest trajectories (HR鈥�=鈥�1.959, 95%CI鈥�=鈥�1.616鈥�2.376) were associated with a higher risk of RA.

In fully adjusted model 3, trajectories of depressive symptoms, physical activity, and multimorbidity status were all associated with the risk of RA. The trajectory of keeping a low rating for depressive symptoms (HR鈥�=鈥�0.649, 95%CI鈥�=鈥�0.533鈥�0.790) and the trajectory of maintaining a moderate rating (HR鈥�=鈥�0.798, 95%CI鈥�=鈥�0.644鈥�0.988) reduced the risk of RA compared to the downward trend trajectory of depressive symptoms. Compared to the lowest trajectory of multimorbidity status, the high trajectory (HR鈥�=鈥�1.305, 95%CI鈥�=鈥�1.094鈥�1.556) and the highest trajectory (HR鈥�=鈥�1.393, 95%CI鈥�=鈥�1.131鈥�1.715) increased the risk of RA. Compared to the keep low trajectory of physical activity, both the high and descending trajectory (HR鈥�=鈥�1.456, 95%CI鈥�=鈥�1.170鈥�1.812) and the rose and High and rising trajectory (HR鈥�=鈥�1.244, 95%CI鈥�=鈥�1.016鈥�1.522) increased the risk of RA (Table 2).

Table 2 Association of trajectories of depressive symptoms, physical activity, and multimorbidity status with rheumatoid arthritis risk
Full size table

Subgroup analysis of trajectories of depressive symptoms, physical activity, and multimorbidity status with risk of rheumatoid arthritis

The subgroup analysis revealed that in males, class_depression1(keep low class) (HR鈥�=鈥�0.554, 95%CI鈥�=鈥�0.386鈥�0.795) and class_Multimorbidity_status3 (high class) (HR鈥�=鈥�1.510, 95%CI鈥�=鈥�1.152鈥�1.979) showed significant associations, with depression reducing the risk and higher multimorbidity increasing the risk of RA. Additionally, class_Multimorbidity_status4 (highest class) (HR鈥�=鈥�1.680, 95%CI鈥�=鈥�1.213鈥�2.327) also had a significant positive effect. In females, class_depression1(keep low class) (HR鈥�=鈥�0.592, 95%CI鈥�=鈥�0.471鈥�0.743) was protective, while class_Multimorbidity_status4(highest class) (HR鈥�=鈥�1.491, 95%CI鈥�=鈥�1.152鈥�1.931) and class_ACTIVITY3(high and descending class) (HR鈥�=鈥�1.788, 95%CI鈥�=鈥�1.329鈥�2.406) significantly increased the risk of RA. Other activity levels, such as class_ACTIVITY4 (HR鈥�=鈥�1.569, 95%CI鈥�=鈥�1.185鈥�2.077) and class_ACTIVITY5(decline rapidly class) (HR鈥�=鈥�1.472, 95%CI鈥�=鈥�1.049鈥�2.066), also showed positive associations with risk. These findings highlight how depression, multimorbidity, and physical activity are linked to RA risk in both genders, with varying effects across different categories听(Fig. 3).

Fig. 3
figure 3

Subgroup analysis of Hazard Ratios (HR) for RA risk by gender

Full size image

Discussion

A prospective cohort study was conducted using seven waves of national data from the Health and Retirement Study (HRS 2004鈥�2018) involving 9,795 US adults. A growth mixture model identified 6-year trajectories from 2004 to 2010, and participants were screened for RA by self-reported physician diagnosis in the subsequent four waves (2010鈥�2018). In fully adjusted model 3, the trajectories of depressive symptoms, physical activity, and multimorbidity status were all associated with the risk of RA. Maintaining a low or moderate rating for depressive symptoms reduced the risk of RA compared to a downward trend. High and highest multimorbidity trajectories increased the risk compared to low trajectories. For physical activity, both descended and maintained high and rose and maintained high trajectories increased the risk compared to low trajectories. These findings suggest that while moderate physical activity may help reduce RA risk, high levels of physical activity, particularly when rising or maintaining at high levels, may increase the risk of RA. This could be due to overexertion or the exacerbation of systemic inflammation, highlighting the complex relationship between physical activity and RA risk.

The study found that a higher multimorbidity status score is associated with an increased risk of developing RA. Specifically, individuals with the highest multimorbidity status trajectory had a significantly higher risk (HR鈥�=鈥�1.393, 95%CI鈥�=鈥�1.131鈥�1.715), and those with a high trajectory also faced an elevated risk (HR鈥�=鈥�1.305, 95%CI鈥�=鈥�1.094鈥�1.556). It has been found that the multimorbid state is often accompanied by a variety of chronic diseases, such as diabetes, hypertension, and cardiovascular disease, which themselves cause a systemic inflammatory response. Chronic systemic inflammation exacerbates the inflammatory process in RA, leading to more severe joint damage and dysfunction, thereby increasing the risk of RA [24]. The structure of a study of 5658 UK Biobank participants suggests that multimorbidity status scores also increase the risk of adverse outcomes in RA patients [25]. Meanwhile, a nationwide population-based case鈥揷ontrol study demonstrated that patients with type 2 diabetes have an elevated risk of RA [26]. At the same time, some have found that hypertension is closely related to systemic inflammation, and there is a positive relationship between increased levels of C reactive protein (CRP) and increased systolic blood pressure [27]. Pro-inflammatory cytokines (e. g., tumor necrosis factor 伪 (TNF 伪), interleukin 1 (IL-1), IL-6), and immune cells (e. g., autoreactive CD4鈥�+鈥塗 cells, B cells, and macrophages) may all play a crucial role with Elevated levels in the pathogenesis of RA [28]. These cytokines create a pro-inflammatory environment that can trigger or exacerbate RA symptoms.

Physical activity is associated with the risk of developing RA. This study found that both the high and descending trajectory (HR鈥�=鈥�1.456, 95%CI鈥�=鈥�1.170鈥�1.812) and the high and rising trajectory (HR鈥�=鈥�1.244, 95%CI鈥�=鈥�1.016鈥�1.522) increased RA risk. Conversely, keeping low trajectory (HR鈥�=鈥�0.649, 95%CI鈥�=鈥�0.533鈥�0.790) and maintaining a moderate trajectory (HR鈥�=鈥�0.798, 95%CI鈥�=鈥�0.644鈥�0.988) significantly reduced RA risk compared to a downward trajectory. In our study, vigorous physical activity was found to increase RA risk. This may be due to its potential to trigger systemic inflammation, which can exacerbate the underlying inflammatory processes in RA. High-intensity exercise can elevate pro-inflammatory cytokines such as TNF-伪 and IL-6, leading to prolonged inflammation, which may contribute to RA development [29]. Conversely, moderate physical activity may help reduce inflammation and lower RA risk.

RA is linked to negative mental health changes, worsening due to inflammatory symptoms and reduced quality of life. RA is linked to negative mental health changes, worsening due to inflammatory symptoms and reduced quality of life. Depression increases RA risk and severity by negatively impacting coping and medication adherence, leading to poorer outcomes [30]. A cohort study found MDD increased RA risk by 38% through elevated pro-inflammatory cytokines, with antidepressants potentially reducing this risk [31]. A review of 32 studies reported significant reductions in IL-4, IL-6, IL-10, and IL-1 after antidepressant treatment [32]. Declining depressive symptoms reduce adherence to antidepressants, causing unstable inflammation and higher RA risk due to negative mental health changes.

While the study found significant associations between depressive symptoms, physical activity, and multimorbidity with RA risk, no significant relationship was found between smoking and RA. This may be due to the short follow-up period, which limited our ability to capture the long-term effects of smoking. Additionally, factors like depressive symptoms and multimorbidity may have had a stronger influence on RA risk in our cohort. Smoking is often linked with chronic conditions like diabetes and hypertension, which may contribute more significantly to the systemic inflammation associated with RA. Future studies with longer follow-up and more detailed assessments of smoking duration and intensity may provide further insight.

Conclusion

This study provides insights into the long-term impact of depressive symptoms, physical activity, and multimorbidity on RA risk. The findings suggest that stable mental health and moderate physical activity may lower RA risk, while high multimorbidity and excessive activity could increase susceptibility.

By refining model selection, addressing bias, and incorporating interaction and subgroup analyses, we have strengthened methodological rigor. While a competing risks model was not applied, its implications are discussed in the limitations.

These findings highlight the importance of long-term health trajectories in RA prevention. Future research should explore these relationships with longer follow-up, external validation, and alternative analytical approaches to enhance generalizability.

Limitations

One limitation of this study is the exclusion of 22,009 participants due to missing data on key predictors such as depressive symptoms and physical activity. While this approach aligns with similar studies and helps maintain the stability of trajectory analyses, it may introduce selection bias. Individuals with missing data could have different health profiles, potentially leading to an overrepresentation of those with fewer risk factors and an overestimation of RA incidence. As a result, the generalizability of our findings may be limited, reflecting only the population with complete data rather than the broader RA burden in the U.S. Future studies should explore appropriate imputation methods or conduct sensitivity analyses to assess the impact of missing data.听The reliance on self-reported data may introduce recall bias and inaccuracies. Additionally, the findings, based on older adults in the US, may not be applicable to other age groups or regions. Furthermore, the assessment intervals may miss short-term fluctuations in depressive symptoms, physical activity, and multimorbidity.

Data availability

The datasets analyzed in this study are publicly available from the Health and Retirement Study (HRS). Researchers can access the data upon request and by adhering to the HRS data use agreements. For more information or to request access, please visit either the main HRS website at .

Abbreviations

aBIC:

Adjusted Bayesian Information Criterion

AIC:

Akaike Information Criterion

BLRT:

Bootstrap Likelihood Ratio Test

BIC:

Bayesian Information Criterion

BMI:

Body mass index

CES-D:

Centers for Epidemiologic Studies Depression

GMM:

Growth mixture model

LMR:

Lo-Mendell-Rubin

MET:

Metabolic equivalents of task

HRS:

Health and Retirement Study

HR:

Hazard ratios

RA:

Rheumatoid Arthritis

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Acknowledgements

The authors thank the Health and Retirement Study team for providing access to the data and their valuable contributions to aging research. We also acknowledge the technical support provided by the Public Data Research Institute and the insightful feedback from reviewers during the drafting process.

Funding

This research received no specific grant from any funding agency, commercial, or not-for-profit sectors.

Author information

Author notes

  1. Mengjun Tao and Xin Guo these authors contributed equally as co-first authors for this article.

Authors and Affiliations

  1. Department of Health Management Center, The First Affiliated Hospital of Wannan Medical College, No. 2, Zheshan West Road, Jinghu District, Wuhu, Anhui Province, China

    Mengjun Tao

  2. School of Public Health, Wannan Medical College, No. 22, Wenchang West Road, Yijiang District, Wuhu City, Anhui Province, China

    Xin Guo,听Xiancan Ji听&听Hui Yuan

  3. Department of Rheumatology and Immunology, The First Affiliated Hospital of Wannan Medical College, No. 2, Zheshan West Road, Jinghu District, Wuhu, Anhui Province, China

    Liang Xu

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  1. Mengjun Tao

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  3. Xiancan Ji

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  4. Liang Xu

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  5. Hui Yuan

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Contributions

Mengjun Tao and Xin Guo (co-first authors) contributed equally to the conceptualization, design, and analysis of the study, and drafted the initial manuscript. Mengjun Tao also led the article鈥檚 revision. Xiancan Ji ensured the accuracy and reliability of the data analysis. Liang Xu provided domain expertise in rheumatoid arthritis and contributed to the interpretation of findings. Hui Yuan supervised the study, provided critical revisions to the manuscript, and secured funding. All authors have reviewed and approved the final manuscript.

Corresponding author

Correspondence to Hui Yuan.

Ethics declarations

Ethics approval and consent to participate

This study used publicly available data from the Health and Retirement Study (HRS), which is conducted with appropriate ethical oversight and informed consent from participants. As this study involved secondary data analysis, additional ethical approval and consent were not required.

Consent for publication

Not applicable. This manuscript does not include individual-level data or identifiable information requiring consent for publication.

Competing interests

The authors declare no competing interests.

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Tao, M., Guo, X., Ji, X. et al. Trajectories of health status and their association with rheumatoid arthritis risk: insights from a national prospective cohort study. 樱花视频 25, 1132 (2025). https://doi.org/10.1186/s12889-025-22303-4

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  • DOI: https://doi.org/10.1186/s12889-025-22303-4

Keywords

樱花视频

ISSN: 1471-2458

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