Digital Approaches for a Reliable Early Diagnosis of Psoriatic Arthritis

Prediction of Psoriatic Arthritis in Patients With Psoriasis Using DNA Methylation Profiles

OBJECTIVE

Psoriatic arthritis (PsA) is an immune-mediated inflammatory arthritis, associated with psoriasis, that significantly increases morbidity and mortality risk. We currently lack the means of predicting which patients with psoriasis will develop PsA, and a large number of patients remain undiagnosed. Regulation of gene expression through DNA methylation can potentially trigger and maintain PsA pathophysiological processes. We aimed to identify DNA methylation markers that can predict which patients with psoriasis will develop PsA prior to the onset of musculoskeletal symptoms.

METHODS

Genome-wide DNA methylation was assessed in blood samples from patients with psoriasis who went on to develop arthritis (converters) and patients with psoriasis who did not (biologic naive, matched for age, sex, psoriasis duration, and duration of follow-up). Methylation differences between converters and nonconverters were identified by a multivariate linear regression model including clinical covariates (age, sex, body mass index, smoking). Predictive performance of methylation markers was assessed by developing support vector machine classification models with and without the addition of clinical variables.

RESULTS

We identified a set of 36 highly relevant methylation markers (false discovery rate: adjusted P < 0.05 and a minimum change in methylation of 0.05) across 15 genes and several intergenic regions. A classification model relying on these markers identified converters and nonconverters with an area under the receiver operating characteristic curve of 0.9644.

CONCLUSION

This study shows that DNA methylation patterns at an early stage of psoriatic disease can distinguish between patients who will develop PsA from those who will not during the same follow-up.

Artificial intelligence in psoriasis: Where we are and where we are going

Artificial intelligence (AI) is a field of computer science that involves the development of programs designed to replicate human cognitive processes and the analysis of complex data. In dermatology, which is predominantly a visual-based diagnostic field, AI has become increasingly important in improving professional processes, particularly in the diagnosis of psoriasis. In this review, we summarized current AI applications in psoriasis: (i) diagnosis, including identification, classification, lesion segmentation, lesion severity and area scoring; (ii) treatment, including prediction treatment efficiency and prediction candidate drugs; (iii) management, including e-health and preventive medicine. Key challenges and future aspects of AI in psoriasis were also discussed, in hope of providing potential directions for future studies.

Harnessing Big Data, Smart and Digital Technologies and Artificial Intelligence for Preventing, Early Intercepting, Managing, and Treating Psoriatic Arthritis: Insights From a Systematic Review of the Literature

Background

Rheumatological and dermatological disorders contribute to a significant portion of the global burden of disease. Big Data are increasingly having a more and more relevant role, being highly ubiquitous and pervasive in contemporary society and paving the way for new, unprecedented perspectives in biomedicine, including dermatology and rheumatology. Rheumatology and dermatology can potentially benefit from Big Data.

Methods

A systematic review of the literature was conducted according to the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA) guidelines, mining “Uno per tutti”, a highly integrated and automated tool/meta-database developed at the University of Genoa, Genoa, Italy, and consisting of 20 major scholarly electronic databases, including PubMed/MEDLINE. Big Data- or artificial intelligence-based studies were judged based on the modified Qiao’s critical appraisal tool for critical methodological quality assessment of Big Data/machine learning-based studies. Other studies designed as cross-sectional, longitudinal, or randomized investigations, reviews/overviews or expert opinions/commentaries were evaluated by means of the relevant “Joanna Briggs Institute” (JBI)’s critical appraisal tool for the critical methodological quality assessment.

Results

Fourteen papers were included in the present systematic review of the literature. Most of the studies included concerned molecular applications of Big Data, especially in the fields of genomics and post-genomics. Other studies concerned epidemiological applications, with a practical dearth of studies assessing smart and digital applications for psoriatic arthritis patients.

Conclusions

Big Data can be a real paradigm shift that revolutionizes rheumatological and dermatological practice and clinical research, helping to early intercept psoriatic arthritis patients. However, there are some methodological issues that should be properly addressed (like recording and association biases) and some ethical issues that should be considered (such as privacy). Therefore, further research in the field is warranted.

Systematic Review Registration

Registration code 10.17605/OSF.IO/4KCU2.

Psoriatic arthritis: prospects for the future

Psoriatic arthritis (PsA) is a form of chronic inflammatory arthritis associated with psoriasis and a multitude of other symptoms, most commonly arthritis, dactylitis, enthesitis and axial involvement. PsA is significantly heterogeneous, with a highly variable clinical course of PsA. Patients may experience significant or mild skin and joint symptoms, with some patients developing rapidly progressing joint destruction and skin symptoms. Despite the range of symptom severity, PsA is frequently associated with significantly impaired quality of life from joint destruction, as well as chronic pain and a range of comorbidities such as depression and cardiovascular disease. Currently, there are no definitive diagnostic tests for PsA, with diagnosis remaining challenging owing to the heterogeneous presentation and course of the disease. Presently, the CASPAR criteria are often used to aid rheumatologists in distinguishing PsA from other inflammatory arthritides. Treatment options for patients have been expanded over the last two decades with the emerging clinical utility of biological therapies. However, early identification and diagnosis of patients and effective disease control remain unmet medical needs within the PsA community. In addition, predicting response to treatment also remains a challenge to rheumatologists. This review highlights the current hurdles faced by healthcare professionals in the diagnosis and management of PsA patients and provides future action points for consideration by the members of the multidisciplinary team who treat PsA patients.

The Digital Way to Intercept Psoriatic Arthritis

Psoriasis (PsO) and Psoriatic Arthritis (PsA) are chronic, immune-mediated diseases that share common etiopathogenetic pathways. Up to 30% of PsO patient may later develop PsA. In nearly 75% of cases, skin psoriatic lesions precede arthritic symptoms, typically 10 years prior to the onset of joint symptoms, while PsO diagnosis occurring after the onset of arthritis is described only in 15% of cases. Therefore, skin involvement offers to the rheumatologist a unique opportunity to study PsA in a very early phase, having a cohort of psoriatic “risk patients” that may develop the disease and may benefit from preventive treatment. Progression from PsO to PsA is often characterized by non-specific musculoskeletal symptoms, subclinical synovio-entheseal inflammation, and occasionally asymptomatic digital swelling such as painless toe dactylitis, that frequently go unnoticed, leading to diagnostic delay. The early diagnosis of PsA is crucial for initiating a treatment prior the development of significant and permanent joint damage. With the ongoing development of pharmacological treatments, early interception of PsA has become a priority, but many obstacles have been reported in daily routine. The introduction of digital technology in rheumatology may fill the gap in the physician-patient relationship, allowing more targeted monitoring of PsO patients. Digital technology includes telemedicine, virtual visits, electronic health record, wearable technology, mobile health, artificial intelligence, and machine learning. Overall, this digital revolution could lead to earlier PsA diagnosis, improved follow-up and disease control as well as maximizing the referral capacity of rheumatic centers.