Explore the vast range of titles available.

Biologic therapies targeting the IL-23/IL-17 axis have transformed the treatment of psoriasis. However, the early mechanisms of action of these drugs remain poorly understood. Here, we perform longitudinal single-cell RNA-sequencing in affected individuals receiving IL-23 inhibitor therapy. By profiling skin at baseline, day 3 and day 14 of treatment, we demonstrate that IL-23 blockade causes marked gene expression shifts, with fibroblast and myeloid populations displaying the most extensive changes at day 3. We also identify a transient WNT5A + /IL24+ fibroblast state, which is only detectable in lesional skin. In-silico and in-vitro studies indicate that signals stemming from these WNT5A + /IL24+ fibroblasts upregulate multiple inflammatory genes in keratinocytes. Importantly, the abundance of WNT5A + /IL24+ fibroblasts is significantly reduced after treatment. This observation is validated in-silico , by deconvolution of multiple transcriptomic datasets, and experimentally, by RNA in-situ hybridization. These findings demonstrate that the evolution of inflammatory fibroblast states is a key feature of resolving psoriasis skin. Single cell profiling of tissue from patients undergoing therapy has the potential to identify drug-induced immune changes. Here the authors show a skin scRNA-seq study of psoriasis patients treated with an IL-23 inhibitor and characterize changes in cell states during early treatment.

Innate lymphoid cells (ILCs) are increasingly appreciated as key regulators of tissue immunity. However, their role in human tissue homeostasis and disease remains to be fully elucidated. Here we characterize the ILCs in human skin from healthy individuals and from the inflammatory skin disease psoriasis. We show that a substantial proportion of IL-17A and IL-22 producing cells in the skin and blood of normal individuals and psoriasis patients are CD3-negative innate lymphocytes. Deep immunophenotyping of human ILC subsets showed a statistically significant increase in the frequency of circulating NKp44+ ILC3 in the blood of psoriasis patients compared with healthy individuals or atopic dermatitis patients. More than 50% of circulating NKp44+ ILC3 expressed cutaneous lymphocyte–associated antigen, indicating their potential for skin homing. Analysis of skin tissue revealed a significantly increased frequency of total ILCs in the skin compared with blood. Moreover, the frequency of NKp44+ ILC3 was significantly increased in non-lesional psoriatic skin compared with normal skin. A detailed time course of a psoriasis patient treated with anti–tumor necrosis factor showed a close association between therapeutic response, decrease in inflammatory skin lesions, and decrease of circulating NKp44+ ILC3. Overall, data from this initial observational study suggest a potential role for NKp44+ ILC3 in psoriasis pathogenesis.

Drug survival reflects a drug’s effectiveness, safety, and tolerability. We assessed the drug survival of biologics used to treat psoriasis in a prospective national pharmacovigilance cohort (British Association of Dermatologists Biologic Interventions Register (BADBIR)). The survival rates of the first course of biologics for 3,523 biologic-naive patients with chronic plaque psoriasis were compared using survival analysis techniques and predictors of discontinuation analyzed using a multivariate Cox proportional hazards model. Data for patients on adalimumab (n=1,879), etanercept (n=1,098), infliximab (n=96), and ustekinumab (n=450) were available. The overall survival rate in the first year was 77%, falling to 53% in the third year. Multivariate analysis showed that female gender (hazard ratio (HR) 1.22; 95% confidence interval (CI): 1.09–1.37), being a current smoker (HR 1.19; 95% CI: 1.03–1.38), and a higher baseline dermatology life quality index (HR 1.01; 95% CI: 1.00–1.02) were predictors of discontinuation. Presence of psoriatic arthritis (HR 0.82; 95% CI: 0.71–0.96) was a predictor for drug survival. As compared with adalimumab, patients on etanercept (HR 1.63; 95% CI: 1.45–1.84) or infliximab (HR 1.56; 95% CI: 1.16–2.09) were more likely to discontinue therapy, whereas patients on ustekinumab were more likely to persist (HR 0.48; 95% CI: 0.37–0.62). After accounting for relevant covariates, ustekinumab had the highest first-course drug survival. The results of this study will aid clinical decision making when choosing biologic therapy for psoriasis patients.

Background Psoriasis and atopic eczema are common inflammatory skin diseases. Existing research has identified increased risks of common mental disorders (anxiety, depression) in people with eczema and psoriasis; however, explanations for the associations remain unclear. We aimed to establish the risk factors for mental illness in those with eczema or psoriasis and identify the population groups most at risk. Methods We used routinely collected data from the UK Clinical Practice Research Datalink (CPRD) GOLD. Adults registered with a general practice in CPRD (1997–2019) were eligible for inclusion. Individuals with eczema/psoriasis were matched (age, sex, practice) to up to five adults without eczema/psoriasis. We used Cox regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for hazards of anxiety or depression in people with eczema/psoriasis compared to people without. We adjusted for known confounders (deprivation, asthma [eczema], psoriatic arthritis [psoriasis], Charlson comorbidity index, calendar period) and potential mediators (harmful alcohol use, body mass index [BMI], smoking status, and, in eczema only, sleep quality [insomnia diagnoses, specific sleep problem medications] and high-dose oral glucocorticoids). Results We identified two cohorts with and without eczema (1,032,782, matched to 4,990,125 without), and with and without psoriasis (366,884, matched to 1,834,330 without). Sleep quality was imbalanced in the eczema cohorts, twice as many people with eczema had evidence of poor sleep at baseline than those without eczema, including over 20% of those with severe eczema. After adjusting for potential confounders and mediators, eczema and psoriasis were associated with anxiety (adjusted HR [95% CI]: eczema 1.14 [1.13–1.16], psoriasis 1.17 [1.15–1.19]) and depression (adjusted HR [95% CI]: eczema 1.11 [1.1–1.12], psoriasis 1.21 [1.19–1.22]). However, we found evidence that these increased hazards are unlikely to be constant over time and were especially high 1-year after study entry. Conclusions Atopic eczema and psoriasis are associated with increased incidence of anxiety and depression in adults. These associations may be mediated through known modifiable risk factors, especially sleep quality in people with eczema. Our findings highlight potential opportunities for the prevention of anxiety and depression in people with eczema/psoriasis through treatment of modifiable risk factors and enhanced eczema/psoriasis management.

Acne vulgaris is a highly heritable skin disorder that primarily impacts facial skin. Severely inflamed lesions may leave permanent scars that have been associated with long-term psychosocial consequences. Here, we perform a GWAS meta-analysis comprising 20,165 individuals with acne from nine independent European ancestry cohorts. We identify 29 novel genome-wide significant loci and replicate 14 of the 17 previously identified risk loci, bringing the total number of reported acne risk loci to 46. Using fine-mapping and eQTL colocalisation approaches, we identify putative causal genes at several acne susceptibility loci that have previously been implicated in Mendelian hair and skin disorders, including pustular psoriasis. We identify shared genetic aetiology between acne, hormone levels, hormone-sensitive cancers and psychiatric traits. Finally, we show that a polygenic risk score calculated from our results explains up to 5.6% of the variance in acne liability in an independent cohort. Better understanding of the genetic basis of acne can pave the way to more effective treatments. Here, the authors perform a genome-wide association study meta-analysis of >20,000 cases and identify 29 new acne susceptibility loci, uncovering genetic links to Mendelian hair and skin disorders and other complex traits.

Skin diseases affect one-third of the global population, posing a major healthcare burden. Deep learning may optimise healthcare workflows through processing skin images via neural networks to make predictions. A focus of deep learning research is skin lesion triage to detect cancer, but this may not translate to the wider scope of >2000 other skin diseases. We searched for studies applying deep learning to skin images, excluding benign/malignant lesions (1/1/2000-23/6/2022, PROSPERO CRD42022309935). The primary outcome was accuracy of deep learning algorithms in disease diagnosis or severity assessment. We modified QUADAS-2 for quality assessment. Of 13,857 references identified, 64 were included. The most studied diseases were acne, psoriasis, eczema, rosacea, vitiligo, urticaria. Deep learning algorithms had high specificity and variable sensitivity in diagnosing these conditions. Accuracy of algorithms in diagnosing acne (median 94%, IQR 86–98; n = 11), rosacea (94%, 90–97; n = 4), eczema (93%, 90–99; n = 9) and psoriasis (89%, 78–92; n = 8) was high. Accuracy for grading severity was highest for psoriasis (range 93–100%, n = 2), eczema (88%, n = 1), and acne (67–86%, n = 4). However, 59 (92%) studies had high risk-of-bias judgements and 62 (97%) had high-level applicability concerns. Only 12 (19%) reported participant ethnicity/skin type. Twenty-four (37.5%) evaluated the algorithm in an independent dataset, clinical setting or prospectively. These data indicate potential of deep learning image analysis in diagnosing and monitoring common skin diseases. Current research has important methodological/reporting limitations. Real-world, prospectively-acquired image datasets with external validation/testing will advance deep learning beyond the current experimental phase towards clinically-useful tools to mitigate rising health and cost impacts of skin disease.

Biologic therapies have transformed the management of psoriasis, but clinical outcome is variable leaving an unmet clinical need for predictive biomarkers of response. Here we perform in-depth immunomonitoring of blood immune cells of 67 patients with psoriasis, before and during therapy with the anti-TNF drug adalimumab, to identify immune mediators of clinical response and evaluate their predictive value. Enhanced NF-κBp65 phosphorylation, induced by TNF and LPS in type-2 dendritic cells (DC) before therapy, significantly correlates with lack of clinical response after 12 weeks of treatment. The heightened NF-κB activation is linked to increased DC maturation in vitro and frequency of IL-17 + T cells in the blood of non-responders before therapy. Moreover, lesional skin of non-responders contains higher numbers of dermal DC expressing the maturation marker CD83 and producing IL-23, and increased numbers of IL-17 + T cells. Finally, we identify and clinically validate LPS-induced NF-κBp65 phosphorylation before therapy as a predictive biomarker of non-response to adalimumab, with 100% sensitivity and 90.1% specificity in an independent cohort. Our study uncovers important molecular and cellular mediators underpinning adalimumab mechanisms of action in psoriasis and we propose a blood biomarker for predicting clinical outcome. Biomarkers to indicate potential response to biologic therapeutics are needed for patients with psoriasis. Here the authors show that phosphorylation of NFκBp65 in cDC2 before therapy is an indication of non-response to the anti-TNF therapy adalimumab in patients with psoriasis.

In view of the growth of clinical risk prediction models using genetic data, there is an increasing need for studies that use appropriate methods to select the optimum number of features from a large number of genetic variants with a high degree of redundancy between features due to linkage disequilibrium (LD). Filter feature selection methods based on information theoretic criteria, are well suited to this challenge and will identify a subset of the original variables that should result in more accurate prediction. However, data collected from cohort studies are often high-dimensional genetic data with potential confounders presenting challenges to feature selection and risk prediction machine learning models. Patients with psoriasis are at high risk of developing a chronic arthritis known as psoriatic arthritis (PsA). The prevalence of PsA in this patient group can be up to 30% and the identification of high risk patients represents an important clinical research which would allow early intervention and a reduction of disability. This also provides us with an ideal scenario for the development of clinical risk prediction models and an opportunity to explore the application of information theoretic criteria methods. In this study, we developed the feature selection and psoriatic arthritis (PsA) risk prediction models that were applied to a cross-sectional genetic dataset of 1462 PsA cases and 1132 cutaneous-only psoriasis (PsC) cases using 2-digit HLA alleles imputed using the SNP2HLA algorithm. We also developed stratification method to mitigate the impact of potential confounder features and illustrate that confounding features impact the feature selection. The mitigated dataset was used in training of seven supervised algorithms. 80% of data was randomly used for training of seven supervised machine learning methods using stratified nested cross validation and 20% was selected randomly as a holdout set for internal validation. The risk prediction models were then further validated in UK Biobank dataset containing data on 1187 participants and a set of features overlapping with the training dataset.Performance of these methods has been evaluated using the area under the curve (AUC), accuracy, precision, recall, F1 score and decision curve analysis(net benefit). The best model is selected based on three criteria: the ‘lowest number of feature subset’ with the ‘maximal average AUC over the nested cross validation’ and good generalisability to the UK Biobank dataset. In the original dataset, with over 100 different bootstraps and seven feature selection (FS) methods, HLA_C_*06 was selected as the most informative genetic variant. When the dataset is mitigated the single most important genetic features based on rank was identified as HLA_B_*27 by the seven different feature selection methods, consistent with previous analyses of this data using regression based methods. However, the predictive accuracy of these single features in post mitigation was found to be moderate (AUC= 0.54 (internal cross validation), AUC=0.53 (internal hold out set), AUC=0.55(external data set)). Sequentially adding additional HLA features based on rank improved the performance of the Random Forest classification model where 20 2-digit features selected by Interaction Capping (ICAP) demonstrated (AUC= 0.61 (internal cross validation), AUC=0.57 (internal hold out set), AUC=0.58 (external dataset)). The stratification method for mitigation of confounding features and filter information theoretic feature selection can be applied to a high dimensional dataset with the potential confounders.

IntroductionTargeted biologic therapies have transformed outcomes for individuals with psoriasis, a common immune-mediated inflammatory skin disease. The widespread use of these highly effective treatments has led to a growing number of individuals with clear or nearly clear skin remaining on continuous, long-term treatment. Personalised strategies to minimise drug exposure may sustain long-term disease control while reducing treatment burden, associated risks and healthcare costs. This study aims to evaluate the feasibility of a definitive pragmatic effectiveness trial of two personalised dose minimisation strategies compared with continuous treatment (standard care) in adults with well-controlled psoriasis receiving the exemplar biologic risankizumab.Methods and analysisThis is a multicentre, assessor-blind, parallel group, open-label randomised controlled feasibility trial in the UK, evaluating two personalised biologic dose minimisation strategies for psoriasis. 90 adults with both physician-assessed and patient-assessed clear or nearly clear skin on risankizumab monotherapy for ≥12 months will be randomised in a 1:1:1 ratio to (1) patient-led ‘as-needed’ treatment, where risankizumab is administered at the first sign of self-assessed psoriasis recurrence, (2) therapeutic drug monitoring-guided treatment, with personalised dosing intervals determined using a pharmacokinetic model or (3) continuous treatment as per standard care, for 12 months. Participants will be invited to submit self-reported outcomes and self-taken photographs every 3 months using a bespoke remote monitoring system (mySkin app) and will attend an in-person assessment at 12 months. They may also request additional patient-initiated follow-up appointments during the trial if needed. The primary outcome is the practicality and acceptability of the two personalised biologic dose minimisation strategies, assessed as a composite measure including recruitment and retention rates, adherence to the assigned strategies and acceptability to both patients and clinicians. The feasibility of collecting healthcare cost and resource utilisation data will also be evaluated to inform a future cost-effectiveness analysis. A nested qualitative study, involving semistructured interviews with patients and clinicians, will explore perspectives on the personalised biologic dose minimisation strategies. These findings will inform the design of a future definitive trial.Ethics and disseminationThis study received ethical approval from the Seasonal Research Ethics Committee (reference 24/LO/0089). Results will be disseminated through scientific conferences, peer-reviewed publications and patient/public engagement events. Lay summaries and infographics will be codeveloped with patient partners to ensure the findings are accessible for the wider public.Trial registration numberISRCTN17922845.