Defining trajectories of response in patients with psoriasis treated with biologic therapies

Exploring disease comorbidities and temporal disease progression of psoriasis: an observational, retrospective, multi-database, cohort study

BACKGROUND

Comorbidities associated with psoriasis are well documented. However, few studies have explored the comorbidity trajectories that patients with psoriasis commonly experience over time. This study reports the 5-year comorbidity trajectories of patients with psoriasis.

OBJECTIVES

To determine the long-term comorbidity trajectories of patients with psoriasis in Denmark.

METHODS

This observational cohort study explored the Danish National Patient Registry (DNPR) between 1999 and 2013 to identify comorbidities diagnosed 5 years prior to or after a psoriasis diagnosis. Comorbidity occurrence in patients with psoriasis (psoriasis cohort) was compared with patients without psoriasis (the N group). Comparison groups, each the same size as the psoriasis cohort, were created by selecting random patients from the N group. If a comorbidity occurrence was higher in more than nine comparison groups than in the psoriasis cohort, it was not analysed and only comorbidities that occurred in ≥ 0·8% of the psoriasis cohort were analysed. The strength of association between a psoriasis diagnosis and a comorbidity diagnosis was measured using relative risk (RR). All psoriasis and comorbidity pairs that achieved RR > 1 (P < 0·001) (known as a Diagnosed Pair) were tested for directionality to identify the sequence of diagnoses using a binomial test. Diagnosed Pairs with a statistically significant direction (Bonferroni corrected P-value < 0·025) were then used to create comorbidity trajectory clusters 5 years before and after a psoriasis diagnosis.

RESULTS

A total of 17 683 patients with psoriasis were compared with 10 000 comparison groups. A total of 121 comorbidities met the minimum criteria that ≥ 0·8% of the psoriasis cohort were diagnosed with the comorbidity within 5 years (before or after) of their psoriasis diagnosis. Thirty-eight of these comorbidities achieved RR > 1 (P < 0·001) with psoriasis, of which 19 achieved a significant direction from psoriasis to a comorbidity (including psoriasis to hypothyroidism), and four achieved a significant direction from a comorbidity diagnosis to a psoriasis diagnosis (including Crohn disease to psoriasis); four of five comorbidity trajectories with three sequential diagnoses achieved an RR > 1 (P < 0·001) and a significant direction from psoriasis to the first comorbidity to the second comorbidity (including psoriasis to hypertension to atrial fibrillation and flutter).

CONCLUSIONS

Comorbidity trajectories may support clinicians in conducting disease risk analyses of patients with psoriasis and help plan optimal treatment to prevent future high-risk comorbidities.

Individualised computational modelling of immune mediated disease onset, flare and clearance in psoriasis

Despite increased understanding about psoriasis pathophysiology, currently there is a lack of predictive computational models. We developed a personalisable ordinary differential equations model of human epidermis and psoriasis that incorporates immune cells and cytokine stimuli to regulate the transition between two stable steady states of clinically healthy (non-lesional) and disease (lesional psoriasis, plaque) skin. In line with experimental data, an immune stimulus initiated transition from healthy skin to psoriasis and apoptosis of immune and epidermal cells induced by UVB phototherapy returned the epidermis back to the healthy state. Notably, our model was able to distinguish disease flares. The flexibility of our model permitted the development of a patient-specific “UVB sensitivity” parameter that reflected subject-specific sensitivity to apoptosis and enabled simulation of individual patients’ clinical response trajectory. In a prospective clinical study of 94 patients, serial individual UVB doses and clinical response (Psoriasis Area Severity Index) values collected over the first three weeks of UVB therapy informed estimation of the “UVB sensitivity” parameter and the prediction of individual patient outcome at the end of phototherapy. An important advance of our model is its potential for direct clinical application through early assessment of response to UVB therapy, and for individualised optimisation of phototherapy regimes to improve clinical outcome. Additionally by incorporating the complex interaction of immune cells and epidermal keratinocytes, our model provides a basis to study and predict outcomes to biologic therapies in psoriasis.

We present a new computer model for psoriasis, an immune-mediated disabling skin disease which presents with red, raised scaly plaques that can appear over the whole body. Psoriasis affects millions of people in the UK alone and causes significant impairment to quality of life, and currently has no cure. Only a few treatments (including UVB phototherapy) can induce temporary remission. Despite our increased understanding about psoriasis, treatments are still given on a ‘trial and error’ basis and there are no reliable computer models that can a) elucidate the mechanisms behind psoriasis onset or flare and b) predict a patient’s response to a course of treatment (e.g., phototherapy) and the likelihood of inducing a period of remission. Our computer model addresses both these needs. First, it explicitly describes the interaction between the immune system and skin cells. Second, our model captures response to therapy at the individual patient level and enables personalised prediction of clinical outcomes. Notably, our model also supports prediction of amending individual UVB phototherapy regimes based on the patient’s initial response that include for example personalised delivery schedules (i.e., 3x weekly vs. 5x weekly phototherapy). Therefore, our work is a crucial step towards precision medicine for psoriasis treatment.

Data-driven prediction of biologic treatment responses in psoriasis: steps towards precision medicine

Linked Article: Geifman et al. Br J Dermatol 2021; 185:825–835.