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Abstract 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.

Abstract 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.

Abstract 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.

Abstract Aim The aim of this study is to assess effect of hospital walking programs on outcomes for older inpatients and to characterize hospital walking dose reported across studies. Design A systematic review and meta‐analysis examining impact of hospital walking and/or reported walking dose among medical‐surgical inpatients. For inclusion, studies were observational or experimental, published in English, enrolled inpatients aged ≥ 65 yrs hospitalized for medical or surgical reasons. Methods Searches of PubMed, CINAHL, Embase, Scopus, NICHSR, OneSearch, ClinicalTrials.gov, and PsycINFO were completed in December 2020. Two reviewers screened sources, extracted data, and performed quality bias appraisal. Results Hospital walking dose was reported in 6 studies and commonly as steps/24 hr. Length of stay (LOS) was a common outcome reported. Difference in combined mean LOS between walking and control groups was −5.89 days. Heterogeneity across studies was considerable (I2 = 96%) suggesting poor precision of estimates. Additional, high‐quality trials examining hospital walking and patient outcomes of older patients is needed.

Background Dupilumab is prescribed in one dosage across adult atopic dermatitis patients. Differences in drug exposure may explain variation in treatment response. Objective Investigating the clinical relevance of dupilumab serum concentration in atopic dermatitis in real-world practice. Methods In two centers (Netherlands, UK), adults treated with dupilumab for atopic dermatitis were evaluated for effectiveness and safety pretreatment and at 2, 12, 24, and 48 weeks; trough serum samples were analyzed for dupilumab concentration at corresponding time points. Results In 149 patients, median dupilumab levels during follow-up ranged from 57.4 to 72.4 μg/mL. Levels showed high inter-patient and low intra-patient variability. No correlation was found between levels and ΔEASI. At 2 weeks, levels of ≥64.1 μg/mL predict EASI ≤7 at 24 weeks (specificity:100%, sensitivity:60%; p = .022). At 12 weeks, ≤32.7 μg/mL predicts EASI >7 at 24 weeks (sensitivity:95%, specificity:26%; p = .011). Inverse correlations were found between baseline EASI and levels at 2, 12, and 24 weeks (r = −0.25 to 0.36; p ≤ .023). Low levels were particularly observed in patients with adverse events, treatment interval deviation, and discontinuation. Conclusion At the on-label dosage, the measured range of dupilumab levels does not seem to yield differences in treatment effectiveness. However, disease activity does seem to influence dupilumab levels - higher baseline disease activity results in lower levels at follow-up.

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.

Abstract 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.

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.

Variation in response to biologic therapy for inflammatory diseases, such as psoriasis, is partly driven by variation in drug exposure. Real‐world psoriasis data were used to develop a pharmacokinetic/pharmacodynamic (PK/PD) model for the first‐line therapeutic antibody ustekinumab. The impact of differing dosing strategies on response was explored. Data were collected from a UK prospective multicenter observational cohort (491 patients on ustekinumab monotherapy, drug levels, and anti‐drug antibody measurements on 797 serum samples, 1,590 measurements of Psoriasis Area Severity Index (PASI)). Ustekinumab PKs were described with a linear one‐compartment model. A maximum effect (Emax) model inhibited progression of psoriatic skin lesions in the turnover PD mechanism describing PASI evolution while on treatment. A mixture model on half‐maximal effective concentration identified a potential nonresponder group, with simulations suggesting that, in future, the model could be incorporated into a Bayesian therapeutic drug monitoring “dashboard” to individualize dosing and improve treatment outcomes.

Frontal fibrosing alopecia (FFA) features lichenoid cutaneous inflammation and scarring hair loss. Here, Tziotzios et al. identify four genetic loci associated with FFA by GWAS followed by Bayesian fine-mapping, co-localisation and HLA imputation which highlights HLA-B*07:02 as a risk factor.

Acne vulgaris is a chronic inflammation of the skin, the genetic basis of which is incompletely understood. Here, Petridis et al. perform GWAS and meta-analysis for acne in 26,722 individuals and identify 12 novel risk loci that implicate structure and maintenance of the skin in severe acne risk.

Psoriasis is a common inflammatory skin disease caused by the interplay between multiple genetic and environmental risk factors. This review summarises recent progress in elucidating the genetic basis of psoriasis, particularly through large genome-wide association studies. We illustrate the power of genetic analyses for disease stratification. Psoriasis can be stratified by phenotype (common plaque versus rare pustular variants), or by outcome (prognosis, comorbidities, response to treatment); recent progress has been made in delineating the genetic contribution in each of these areas. We also highlight how genetic data can directly inform the development of effective psoriasis treatments.