Resources
Our team of communicators work hard to translate all the science behind AutoPiX and bring the power of imaging biomarkers closer to the non-expert community, including patients. We are constantly developing resources to help you speed-up with the AutoPiX findings including a glossary, webinars, and summaries of readings.
Readings
Distinguishing rheumatoid arthritis from psoriatic arthritis
Merola JF, Espinoza LR, Fleischmann R. Distinguishing rheumatoid arthritis from psoriatic arthritis.
RMD Open 2018;4:e000656.
doi:10.1136/rmdopen-2018-000656
Differentiating between RA and PsA can be challenging because both diseases present many similarities.
Rheumatoid arthritis (RA) and psoriatic arthritis (PsA) are autoimmune systemic inflammatory diseases characterised by pain and swelling in the joints with systemic manifestations. Joint involvement is predominantly symmetric in RA and often asymmetric in PsA. Most patients have polyarthritis (≥5 involved joints), although the involvement can be oligo or polyarticular. The involvement of the axial skeleton in PsA can be a differentiating feature because it is not present in RA other than cervical spine involvement. Enthesitis, dactylitis and nail dystrophy are common in PsA. Ocular disorders present as keratoconjunctivitis sicca in RA, and as uveitis in PsA. Cutaneous manifestations are common in RA (rheumatoid nodules) and PsA (psoriasis). RA is seropositive for RF or CCP antibodies and PsA is seronegative. Comorbidities linked to systemic inflammation (e.g. cardiovascular disease) are common in both, although the burden may be higher in RA. Obesity, diabetes and metabolic syndrome are significantly higher in PsA.
Although the pathogenesis of RA and PsA is not entirely understood, different factors are thought to trigger an autoimmune inflammatory response. In both RA and PsA, inflammatory responses are characterised by the increased production of proinflammatory molecules associated with joint damage, radiographic progression, and gut inflammation, but different pathways are involved.
Because of the differences in pathogenesis, clinical manifestations, and response to therapy between RA and PsA, treatment strategies may differ.
Why is this important for AutoPiX?
The key differentiating features between RA and PsA—such as the presence of psoriasis, joint involvement patterns, and specific biomarkers (e.g., seropositivity for RA vs. seronegativity for PsA)—could be incorporated into an AI diagnostic system that automatically identifies the type of arthritis based on patient data.
Sex-specific differences and how to handle them in early psoriatic arthritis
E Passia, M Vis, L C Coates, A Soni, I Tchetverikov, A H Gerards et al.
Arthritis Research & Therapy 2022; 24:22.
Doi: 10.1186/s13075-021-02680-y
This study highlights significant sex-specific differences in the clinical manifestations and treatment outcomes of early psoriatic arthritis, suggesting a need for tailored treatment approaches.
Sex and health is a new area of study, aiming to investigate the differences between men and women in both health an disease. Sex has been shown to affect natural history, clinical manifestations, and response to medications in several rheumatic diseases. Although the prevalence of psoriatic arthritis (PsA) is considered equal in men and women, the are not equally affected, with women experiencing a higher burden of disease (pain, disability and fatigue).
The objective of this paper is to assess sex-related differences in patients with newly diagnosed PsA and evaluate the evolution over the first year, in terms of disease activity and health-related quality of life.
The study was conducted on 620 patients (307 men and 313 women). There were no differences in the age at onset of PsA but women reported longer duration of symptoms before diagnosis, less frequency of paid employment and higher prevalence of obesity. At baseline, women presented higher tender joint counts, more frequency of axial disease and worse results in composited indices than men. Fatigue, anxiety and comorbid medical conditions were also more common in women who suffered more severe limitations in function and worse quality of life compared with men. On the contrary, skin lesions were more common in men. At 1 year of follow-up men remained in minimal disease activity and remission more frequently than women. Despite the improvement presented by both sex through 1 year follow-up, women reported higher levels of pain and worse functional status. Cumulative doses of specific drugs were lower in women despite the higher disease burden observed, may be explained by a more frequent reporting of side effects compared to men.
Overall, women presented higher disease activity, pain, and functional impairment compared to men at baseline but also at 1 year of follow-up. Women seem to be undertreated. The nature of these findings could suggest that sex bias in prescribing exists and may advocate the need for sex specific adjustment of treatment strategies and evaluation of Psa.
The study underscores the importance of considering sex-specific differences in the management of psoriatic arthritis, as women experience a higher disease burden and may be undertreated due to differences in symptom reporting and treatment tolerance.
Why is this important for AutoPiX?
The findings from this study on sex-specific differences in early psoriatic arthritis are highly relevant to the AutoPix project, as they emphasize the need for personalized and gender-sensitive approaches in disease management. By understanding these differences, AutoPix can develop more effective diagnostic and therapeutic strategies that account for the unique experiences of both men and women with psoriatic arthritis, potentially improving treatment outcomes and quality of life for all patients. This could involve integrating sex-specific data into AI-driven diagnostic tools and treatment planning algorithms to ensure more tailored care.
Precision medicine and management of rheumatoid arthritis
Aletaha D. J
Autoimmun 2020 Jun:110:102405.
Doi: 10.1016/j.jaut.2020.102405
Exploring the relevance of precision medicine in rheumatoid arthritis for the AutoPiX project, highlighting the integration of AI and big data for personalized treatment approaches.
Precision medicine (PM) is a commonly used term that implies a highly individualized and tailored approach to patient management. PM implies using the unique molecular characteristics of a patient for management decisions. This discipline has moved from using information of an individual patient to selecting the best existing therapy to target discovery by identifying subgroups of patients with specific characteristics, for which targeted therapies can possibly be developed. In clinical practice profiling patients is only useful, if this subsequently can allow targeting the patient’s disease more precisely therapeutically. Examples of precision medicine from oncology may define the potential for other diseases such as rheumatoid arthritis. Oncologists already use therapy with autologous T cells which have a genetically engineered chimeric antigen receptor (so called, CAR-T cells) that can specifically target and kill respective tumor cells. These compounds are precise in individual patients but at the same time, are highly costly; therefore, adequate patient selection clearly needs to be scrutinized. One consequence of precision medicine approaches is a growing need for genetic counseling, and providers are lining up to allow the identification of the right patient for these approaches.
Big data and precision medicine are terms that commonly go together. Today, the presence of large-scale biologic databases and powerful platforms for characterizing patients have created an incredible amount of potentially available information from each patient. At the same time the computation tools for analyzing this amount of data have expanded dramatically.
In addition to molecular sources, big data can also come from other sources (environment, sociodemographic, nutrition) and technologies (electronic health record, mobile phone applications, and wearables). Once obtained, the next step is to integrate these heterogeneous data bioinformatically. The integrated “big data” can then be analyzed using different approaches like machine learning, or network based and statistical methods.
The four major areas for precision medicine in RA include diagnosis, prognosis, treatment selection and treatment reduction. Precision medicine and big data are a fast-evolving field which, despite the current limitations regarding practical relevance, will likely change rheumatology in the years to come.
The convergence of precision medicine and AI in rheumatoid arthritis enhances personalized care and aligns with AutoPiX's mission to improve diagnostic and therapeutic outcomes.
Why is this important for AutoPiX?
The article by Aletaha D. is important for the AutoPiX project because it highlights the potential of precision medicine in managing rheumatoid arthritis (RA), emphasizing personalized treatment approaches based on individual patient characteristics. This aligns with AutoPiX's goal of using AI to enhance imaging biomarkers for RA, facilitating more precise diagnosis and treatment decisions. The article also discusses the role of big data and computational tools like machine learning in precision medicine, which is similar to how AutoPiX leverages AI to analyze imaging data and convert it into meaningful biomarkers. Both the article and the AutoPiX project focus on addressing critical unmet needs in rheumatology, such as improving access to advanced imaging techniques and selecting appropriate treatments. By enhancing imaging biomarkers, AutoPiX contributes to the future of rheumatology, where precision medicine is expected to play a significant role, as suggested by the article