The Manchester Molecular Pathology Innovation Centre, University of Manchester, Manchester, UK.
Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
Br J Dermatol. 2021 Oct;185(4):825-835. doi: 10.1111/bjd.20140. Epub 2021 Jun 4.
The effectiveness and cost-effectiveness of biologic therapies for psoriasis are significantly compromised by variable treatment responses. Thus, more precise management of psoriasis is needed.
To identify subgroups of patients with psoriasis treated with biologic therapies, based on changes in their disease activity over time, that may better inform patient management.
We applied latent class mixed modelling to identify trajectory-based patient subgroups from longitudinal, routine clinical data on disease severity, as measured by the Psoriasis Area and Severity Index (PASI), from 3546 patients in the British Association of Dermatologists Biologics and Immunomodulators Register, as well as in an independent cohort of 2889 patients pooled across four clinical trials.
We discovered four discrete classes of global response trajectories, each characterized in terms of time to response, size of effect and relapse. Each class was associated with differing clinical characteristics, e.g. body mass index, baseline PASI and prevalence of different manifestations. The results were verified in a second cohort of clinical trial participants, where similar trajectories following the initiation of biologic therapy were identified. Further, we found differential associations of the genetic marker HLA-C*06:02 between our registry-identified trajectories.
These subgroups, defined by change in disease over time, may be indicative of distinct endotypes driven by different biological mechanisms and may help inform the management of patients with psoriasis. Future work will aim to further delineate these mechanisms by extensively characterizing the subgroups with additional molecular and pharmacological data.
生物疗法治疗银屑病的有效性和成本效益因治疗反应的差异而受到显著影响。因此，需要更精确地管理银屑病。
基于患者疾病活动度随时间的变化，确定接受生物疗法治疗的银屑病患者亚组，以便更好地指导患者管理。
我们应用潜在类别混合模型，从英国皮肤科医师协会生物制剂和免疫调节剂登记处的 3546 名患者以及四项临床试验的 2889 名患者的纵向常规临床数据（通过银屑病面积和严重程度指数[PASI]测量）中，基于疾病严重程度识别轨迹的患者亚组。
我们发现了四种离散的总体反应轨迹类别，每个类别都有其反应时间、效应大小和复发的特点。每个类别都与不同的临床特征相关，例如体重指数、基线 PASI 和不同表现的患病率。这些结果在第二项临床试验参与者队列中得到了验证，在该队列中，生物治疗开始后也确定了类似的反应轨迹。此外，我们发现 HLA-C*06:02 遗传标记在我们的登记处确定的轨迹之间存在不同的关联。
这些亚组由疾病随时间的变化定义，可能表明由不同生物学机制驱动的不同终末表型，并可能有助于指导银屑病患者的管理。未来的工作将通过使用额外的分子和药理学数据对亚组进行广泛的特征描述，进一步阐明这些机制。
