Dear Editor,
Rheumatoid arthritis (RA), long perceived as a singular autoimmune disorder, may in fact represent a central node within a broader continuum of interconnected diseases, hereafter referred to as “disease continuum.” RA is increasingly recognized as being linked to a variety of comorbidities, including systemic lupus erythematosus (SLE) and type 1 diabetes [
1,
2]. Moreover, evidence suggests that infections, such as COVID-19, may trigger the production of autoantibodies, e.g., antinuclear antibodies (ANA) [
3], anti-cardiolipin, anti-β2-glycoprotein I, anti-citrullinated protein antibodies (ACPA) [
4,
5], perinuclear anti-neutrophil cytoplasmic antibodies (p-ANCA), cytoplasmic ANCA (c-ANCA), anti-nuclear ribonucleoprotein (anti-RNP), anti-centromere, and rheumatoid factor (RF) [
6], potentially contributing to the flaring of RA [
7]. These observations suggest that RA once viewed in isolation may instead be part of a larger disease continuum.
Historically, diseases have been classified into distinct categories. However, the growing studies of disease continuum highlight its dynamic, ripple-like effect where the progression of one disease may cascade into others [
8–
10]. The disease-wide association study (DWAS) is an innovative approach designed to explore the disease continuum [
8]. By exploring the disease continuum, researchers can uncover novel therapeutic targets that address the root causes of disease, offering a more comprehensive approach to healthcare.
Using DWAS, this study provides an enhanced mapping of RA comorbidities, illustrating its role within a broader disease continuum. This shift in perspective holds significant implications for precision medicine, suggesting that further genetic and molecular profiling can better assess RA risks in relation to its comorbidities.
This study utilized data from the FinnGen cohort, which includes 392 423 participants, of whom 12 555 have been diagnosed with RA. The cohort offers a rich data set, encompassing 1289 distinct medical events and clinical outcomes. The cohort was sourced from multiple Finnish national health registries, including the Care Register for Health Care (HILMO), the Population Register (DVV), the Death Registry, the Finnish Cancer Registry, and the Drug Purchase and Reimbursement Database (Kela) [
11]. This data set enables an in-depth analysis of both pre- and post-RA comorbidities. Follow-up data spans from January 1, 1998 to December 31, 2021, or until death, whichever occurred first (; Supplementary Material and Methods).
The cohort data used in this study was derived from de-identified, pre-existing data from the FinnGen study, and as such, ethical approval and patient consent were not required. Nonetheless, strict protocols for data privacy and confidentiality were adhered to in compliance with established ethical standards. No identifiable patient information was included or reported in this study; only aggregated disease association data from the DWAS were reported. All data handling and analysis followed rigorous anonymization procedures, ensuring compliance with data protection regulations.
To identify comorbidities associated with RA, this study applied Cox proportional hazards regression models, adjusting for age and sex to control for potential confounders [
12]. A medical event was considered as a comorbidity if it showed a statistically significant association with RA, determined by a false discovery rate (FDR)-adjusted
P value of less than 0.05. This analysis distinguished between pre-RA comorbidities—those that may contribute to RA onset—and post-RA comorbidities—those that develop after RA diagnosis. High-risk comorbidities were defined based on their statistical significance, prevalence, and strength of association, including an adjusted
P value of less than 0.05, an absolute log
10(hazard ratio) greater than 1.5, and a prevalence above 1.0%, thereby representing the most strongly associated and common comorbidities within the RA population, in order to prioritize the most clinically significant comorbidities for further investigation.
To systematically classify RA comorbidities, this study employed the International Statistical Classification of Diseases and Related Health Problems (ICD-10), the International Classification of Diseases for Oncology (ICD-O-3), and the FinnGen disease classification system to categorize RA comorbidities. This approach allowed us to group comorbidities by disease type across different organ systems, as well as to segment them based on their temporal relationship to RA onset. As such, this study identified pre-RA comorbidities that might predispose individuals to RA, and post-RA comorbidities that develop as a consequence of RA onset. This classification framework provides a clearer view of how RA is interconnected with other diseases, revealing patterns that influence RA’s onset, progression, and patient outcomes [
8].
Using established DWAS analysis, this study identified that RA was associated with an increased prevalence of disorders affecting the musculoskeletal, digestive, respiratory, and circulatory systems (), further demonstrating that RA is not an isolated autoimmune disease but part of a broader disease continuum affecting multiple organ systems.
To examine the evolution of RA comorbidities over time, I visualized their persistence using heatmaps tracking comorbidity occurrence across short-term (1 year), mid-term (5 years), and long-term (15 years) periods. For example, cardiovascular and pulmonary diseases—such as pulmonary embolism, myocardial infarction, and cardiac arrhythmias—emerged predominantly after RA diagnosis, indicating that RA onset may heighten the risk for these diseases (). Furthermore, other post-RA comorbidities, such as renal tubulo–interstitial disorders, showed the widespread impact of RA on different organ systems beyond joint inflammation and damage. These sequential patterns underscore the notion of a disease continuum centered on RA, suggesting that early identification and management of RA-related comorbidities may improve patient outcomes.
This study highlights RA’s role as a central node within the disease continuum (). The sequential patterns between pre- and post-RA comorbidities imply the causal role of RA, where it not only arises from underlying health issues but also exacerbates or leads to the development of additional comorbidities. This calls for a broader, systemic perspective, recognizing RA as both a consequence and a driver for further health issues. The presence of post-RA comorbidities, such as cardiovascular, pulmonary, and kidney diseases, underscores RA’s far-reaching disturbance on health, showing the systemic impact of chronic inflammation. Early identification and intervention of RA comorbidities may provide opportunities to prevent further downward spiral of health deterioration.
This study reframes RA not as an isolated autoimmune disorder, but as part of a broader disease continuum. The concept of disease continuum enables a more comprehensive understanding of RA as part of a dynamic system of associated comorbidities, creating fluid boundaries between different diseases. By adopting this framework, clinicians can realize the need for a more comprehensive approach to RA management, addressing both the disease itself and its associated systemic risks.
Thus, this study provides a compelling case for the shift from a singular to a systemic view of RA. By embracing the concept of disease continuum, we can shift from the traditional “one-size-fits-all” model of healthcare toward more personalized, precision medicine, thus ultimately improving long-term outcomes for RA patients and those affected by related comorbidities.
While the FinnGen cohort provides a comprehensive data set, the findings may be limited in the generalizability to populations outside of Finland. The data are reflective of the Finnish healthcare system but might not capture differences in genetic, environmental, and lifestyle factors in other populations. Additionally, the retrospective nature of the study limits our ability to definitively establish causality, as well as potential confounders such as variations in healthcare access and diagnosis practices, which should be considered when interpreting the results.
This study positions RA as a central node in the disease continuum, highlighting its associations with a wide range of comorbidities. The DWAS approach proves to be a powerful tool for mapping disease associations at an unprecedented scale, paving the way for future research into the genetic and molecular mechanisms that link RA to other diseases. By adopting a broader, systemic view of RA, we can move toward more personalized interventions that improve outcomes for RA patients and those affected by related comorbidities. This approach marks a significant step forward in precision medicine, where treatments are tailored to an individual’s unique comorbidity landscape.
PDF
(3075KB)
