Background: Chronic spontaneous urticaria (CSU) is one of the most intractable human-specific skin diseases. However, as no experimental animal model exists, the mechanism underlying disease pathogenesis in vivo remains unclear, making the establishment of a curative treatment challenging.
Methods: A novel approach combining mathematical modelling, in vitro experiments and clinical data analysis was used to infer the pathological state of CSU patients from geometric features of the skin eruptions.
Results: Based on our hierarchical mathematical modelling, the eruptions of CSU were classified into five categories, each with distinct histamine, basophils, mast cells and coagulation factors network signatures. The analysis of 105 real CSU patients with this classification by six individual dermatologists achieved 87.6% agreement. Furthermore, our network analysis revealed that the coagulation status likely determines boundary/area pattern of wheals, while the state of spontaneous histamine release from mast cells may contribute to the divergence of size and outline of the eruptions.
Conclusions: Our multi-faceted approach was accurate in defining pathophysiological states of disease based on geometric features offering the potential to improve the accuracy of CSU diagnosis and better management of the disease in the clinic.
Chronic spontaneous urticaria (CSU) is a persistent skin disease that causes red itchy skin eruptions, called wheals, that are of various shapes. These wheals repeatedly appear and disappear daily for up to weeks or even decades, severely impacting the quality of life of those affected. The causes, consequences or disease processes are largely understudied. Here, we developed a novel approach using mathematical modelling to analyse the geometric measurements of patients’ wheals alongside laboratory experiments on human skin samples with eruptions to improve CSU diagnosis and clinical management. We find that geometric measurements of these skin eruptions can be classified into five categories, which could facilitate accurate diagnosis and consequently better clinical management of CSU and may be more widely applied to other skin diseases.
© 2023. The Author(s).