Can serum biomarkers predict the outcome of systemic therapy for atopic dermatitis?

SUMMARYBackgroundAtopic dermatitis (AD or eczema) is a most common chronic skin disease. Designing personalised treatment strategies for AD based on patient stratification, rather than the “one-size-fits-all” treatments, is of high clinical relevance. It has been hypothesised that the measurement of biomarkers could help predict therapeutic response for individual patients.ObjectiveWe aim to assess whether biomarkers can predict the outcome of systemic therapy.MethodsWe developed a statistical machine learning predictive model using the data of an already published longitudinal study of 42 patients who received systemic therapy. The data contained 26 serum cytokines measured before the therapy. The model described the dynamics of the latent disease severity and measurement errors to predict AD severity scores (EASI, (o)SCORAD and POEM) two-weeks ahead. We conducted feature selection to identify the most important biomarkers for predicting the AD severity scores.ResultsWe validated our model and confirmed that it outperformed standard time-series forecasting models. Adding biomarkers did not improve predictive performance. Our estimates of the minimum detectable change for the AD severity scores were larger than already published estimates of the minimal clinically important difference.ConclusionsBiomarkers had a negligible and non-significant effect for predicting the future AD severity scores and the outcome of the systemic therapy. Instead, a historical record of severity scores provides rich and insightful dynamical information required for prediction of therapeutic responses.

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Impact of environmental factors in predicting daily severity scores of atopic dermatitis

10.1101/2020.10.27.20220947 ◽

2020 ◽

Author(s):

Guillem Hurault ◽

Valentin Delorieux ◽

Young-Min Kim ◽

Kangmo Ahn ◽

Hywel C. Williams ◽

...

Keyword(s):

Atopic Dermatitis ◽

Environmental Factors ◽

Air Pollutants ◽

Panel Study ◽

Predictive Performance ◽

Statistical Machine Learning ◽

Short Term ◽

Mixed Effect ◽

Severity Scores ◽

Term Basis

ABSTRACTBackgroundAtopic dermatitis (AD) is a chronic inflammatory skin disease that affects 20% of children worldwide. Although environmental factors including weather and air pollutants have been shown to be associated with AD symptoms, the time-dependent nature of such a relationship has not been adequately investigated.ObjectiveThis paper aims to assess the short-term impact of weather and air pollutants on AD severity scores.MethodsUsing longitudinal data from a published panel study of 177 paediatric patients followed up for 17 months, we developed statistical machine learning models to predict daily AD severity scores for individual study participants. Exposures consisted of daily meteorological variables and concentrations of air pollutants and outcomes were daily recordings of scores for six AD signs. We developed a mixed effect autoregressive ordinal logistic regression model, validated it in a forward-chaining setting, and evaluated the effects of the environmental factors on the predictive performance.ResultsOur model outperformed benchmark models for daily prediction of the AD severity scores. The predictive performance of AD severity scores was not improved by the addition of measured environmental factors. Any potential short-term influence of environmental exposures on AD severity scores was outweighed by the underlying persistence of preceding scores.ConclusionsOur data does not offer enough evidence to support a claim that AD symptoms are associated with weather or air pollutants on a short-term basis. Inferences about the magnitude of the effect of environmental factors require consideration of the time-dependence of the AD severity scores.

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Personalised prediction of daily eczema severity scores using a mechanistic machine learning model

10.1101/2020.01.16.20017772 ◽

2020 ◽

Cited By ~ 1

Author(s):

Guillem Hurault ◽

Elisa Domínguez-Hüttinger ◽

Sinéad M. Langan ◽

Hywel C. Williams ◽

Reiko J. Tanaka

Keyword(s):

Machine Learning ◽

Predictive Model ◽

Treatment Strategies ◽

Learning Model ◽

Patient Specific ◽

Topical Steroids ◽

Individual Level ◽

Machine Learning Model ◽

Severity Scores ◽

Personalised Treatment

ABSTRACTBackgroundAtopic dermatitis (AD) is a chronic inflammatory skin disease with periods of flares and remission. Designing personalised treatment strategies for AD is challenging, given the apparent unpredictability and large variation in AD symptoms and treatment responses within and across individuals. Better prediction of AD severity over time for individual patients could help to select optimum timing and type of treatment for improving disease control.ObjectiveWe aimed to develop a mechanistic machine learning model that predicts the patient-specific evolution of AD severity scores on a daily basis.MethodsWe designed a probabilistic predictive model and trained it using Bayesian inference with the longitudinal data from two published clinical studies. The data consisted of daily recordings of AD severity scores and treatments used by 59 and 334 AD children over 6 months and 16 weeks, respectively. Internal and external validation of the predictive model was conducted in a forward-chaining setting.ResultsOur model was able to predict future severity scores at the individual level and improved chance-level forecast by 60%. Heterogeneous patterns in severity trajectories were captured with patient-specific parameters such as the short-term persistence of AD severity and responsiveness to topical steroids, calcineurin inhibitors and step-up treatment.ConclusionOur proof of principle model successfully predicted the daily evolution of AD severity scores at an individual level, and could inform the design of personalised treatment strategies that can be tested in future studies.

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Current Insights into Immunology and Novel Therapeutics of Atopic Dermatitis

Cells ◽

10.3390/cells10061392 ◽

2021 ◽

Vol 10 (6) ◽

pp. 1392

Author(s):

Hidaya A. Kader ◽

Muhammad Azeem ◽

Suhib A. Jwayed ◽

Aaesha Al-Shehhi ◽

Attia Tabassum ◽

...

Keyword(s):

Atopic Dermatitis ◽

Skin Diseases ◽

Current Knowledge ◽

Treatment Strategies ◽

Developed Countries ◽

Food Allergies ◽

Th2 Response ◽

Therapeutic Outcomes ◽

Environmental Agents ◽

Novel Treatment Strategies

Atopic dermatitis (AD) is one of the most prevalent inflammatory disease among non-fatal skin diseases, affecting up to one fifth of the population in developed countries. AD is characterized by recurrent pruritic and localized eczema with seasonal fluctuations. AD initializes the phenomenon of atopic march, during which infant AD patients are predisposed to progressive secondary allergies such as allergic rhinitis, asthma, and food allergies. The pathophysiology of AD is complex; onset of the disease is caused by several factors, including strong genetic predisposition, disrupted epidermal barrier, and immune dysregulation. AD was initially characterized by defects in the innate immune system and a vigorous skewed adaptive Th2 response to environmental agents; there are compelling evidences that the disorder involves multiple immune pathways. Symptomatic palliative treatment is the only strategy to manage the disease and restore skin integrity. Researchers are trying to more precisely define the contribution of different AD genotypes and elucidate the role of various immune axes. In this review, we have summarized the current knowledge about the roles of innate and adaptive immune responsive cells in AD. In addition, current and novel treatment strategies for the management of AD are comprehensively described, including some ongoing clinical trials and promising therapeutic agents. This information will provide an asset towards identifying personalized targets for better therapeutic outcomes.

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Clinical and Therapeutic Trial for the Efficacy of Narrow Band - UVB Phototherapy versus Systemic Therapy in Moderate and Severe Atopic Dermatitis of the Adult

Acta Medica Marisiensis ◽

10.2478/amma-2018-0003 ◽

2018 ◽

Vol 64 (1) ◽

pp. 17-21

Author(s):

Gyula Laszlo Fekete ◽

László Fekete

Keyword(s):

Atopic Dermatitis ◽

Side Effects ◽

Systemic Therapy ◽

Systemic Corticosteroid ◽

Corticosteroid Treatment ◽

Serum Levels ◽

Severe Atopic Dermatitis ◽

Therapeutic Trial ◽

Group I ◽

Uvb Phototherapy

AbstractObjectives: The aim of this clinical and therapy study was to evaluate the efficacy of NB-UVB phototherapy versus systemic therapy in moderate-to-severe atopic dermatitis of the adult.Material and methods: The subjects of the study were divided into two groups of 25 adult patients with moderate and severe atopic dermatitis according to the inclusion criteria. The first group of 25 patients were treated with systemic corticosteroids while the second group of 25 patients were treated with NB-UVB phototherapy. At the end of the study, after all the data were centralized, we performed a statistical analysis of the results, comparing the two groups as well as the efficacy of the different therapies.Results: In group I the clinical efficacy of the systemic corticosteroid treatment was achieved, on average, at 4 weeks in patients with moderate atopic dermatitis and at 6 weeks in patients with severe atopic dermatitis. In group II the clinical effecacy of NB-UVB phototherapy was achieved, on average, at 6 weeks for patients with moderate atopic dermatitis and at 8 weeks for those with the severe form. In both groups, the total IgE serum levels were elevated at the beginning, and they became normal throughout the clinical improvement. Remarkable therapy-related side effects were found in the first study group.Conclusion: We conclude that NB-UVB phototherapy had similar efficacy in treating moderate-to-severe atopic dermatitis with minimal side effects compared to systemic corticosteroid therapy.

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Predicting the future development of mild cognitive impairment in the cognitively healthy elderly

10.1101/2020.07.30.227496 ◽

2020 ◽

Author(s):

Bryan Strange ◽

Linda Zhang ◽

Alba Sierra-Marcos ◽

Eva Alfayate ◽

Jussi Tohka ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Verbal Memory ◽

Statistical Tests ◽

Treatment Strategies ◽

Area Under The Curve ◽

Predictive Performance ◽

Healthy Individuals ◽

Healthy Elderly ◽

Out Of Sample

Identifying measures that predict future cognitive impairment in healthy individuals is necessary to inform treatment strategies for candidate dementia-preventative and modifying interventions. Here, we derive such measures by studying converters who transitioned from cognitively normal at baseline to mild-cognitive impairment (MCI) in a longitudinal study of 1213 elderly participants. We first establish reduced grey matter density (GMD) in left entorhinal cortex (EC) as a biomarker for impending cognitive decline in healthy individuals, employing a matched sampling control for several dementia risk-factors, thereby mitigating the potential effects of bias on our statistical tests. Next, we determine the predictive performance of baseline demographic, genetic, neuropsychological and MRI measures by entering these variables into an elastic net-regularized classifier. Our trained statistical model classified converters and controls with validation Area-Under-the-Curve>0.9, identifying only delayed verbal memory and left EC GMD as relevant predictors for classification. This performance was maintained on test classification of out-of-sample converters and controls. Our results suggest a parsimonious but powerful predictive model for MCI development in the cognitively healthy elderly.

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Genetic and Epigenetic Aspects of Atopic Dermatitis

International Journal of Molecular Sciences ◽

10.3390/ijms21186484 ◽

2020 ◽

Vol 21 (18) ◽

pp. 6484 ◽

Cited By ~ 1

Author(s):

Bogusław Nedoszytko ◽

Edyta Reszka ◽

Danuta Gutowska-Owsiak ◽

Magdalena Trzeciak ◽

Magdalena Lange ◽

...

Keyword(s):

Atopic Dermatitis ◽

Treatment Strategies ◽

Structural Proteins ◽

Epigenetic Changes ◽

Epigenetic Alterations ◽

Adaptive Immune ◽

Genes Encoding ◽

Inflammatory Processes ◽

Non Coding Rnas

Atopic dermatitis is a heterogeneous disease, in which the pathogenesis is associated with mutations in genes encoding epidermal structural proteins, barrier enzymes, and their inhibitors; the role of genes regulating innate and adaptive immune responses and environmental factors inducing the disease is also noted. Recent studies point to the key role of epigenetic changes in the development of the disease. Epigenetic modifications are mainly mediated by DNA methylation, histone acetylation, and the action of specific non-coding RNAs. It has been documented that the profile of epigenetic changes in patients with atopic dermatitis (AD) differs from that observed in healthy people. This applies to the genes affecting the regulation of immune response and inflammatory processes, e.g., both affecting Th1 bias and promoting Th2 responses and the genes of innate immunity, as well as those encoding the structural proteins of the epidermis. Understanding of the epigenetic alterations is therefore pivotal to both create new molecular classifications of atopic dermatitis and to enable the development of personalized treatment strategies.

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Neoadjuvant therapy for breast cancer: updates and proceedings from the Seventh Annual Meeting of the Canadian Consortium for Locally Advanced Breast Cancer

Current Oncology ◽

10.3747/co.25.4153 ◽

2018 ◽

Vol 25 (5) ◽

Cited By ~ 6

Author(s):

A. Arnaout ◽

J. Lee ◽

K. Gelmon ◽

B. Poirier ◽

F. I. Lu ◽

...

Keyword(s):

Breast Cancer ◽

Advanced Breast Cancer ◽

Systemic Therapy ◽

Locally Advanced Breast Cancer ◽

Locally Advanced ◽

Treatment Strategies ◽

Advanced Breast ◽

Treatment Modalities ◽

Neoadjuvant Systemic Therapy ◽

Academic Debate

Therapy for breast cancer involves a complex interplay of three main treatment modalities: surgery, systemic therapy, and radiation therapy. The Canadian Consortium for Locally Advanced Breast Cancer (LABC) was established with the goal to convene a strong multidisciplinary team of breast oncology clinicians and scientists who are dedicated to the advancement of labc research and treatment, with a vision to drive progress through increased collaboration across disciplines and throughout Canada. The most recent meeting in May 2017 highlighted the latest evidence and literature about the optimal use of neoadjuvant systemic therapy in breast cancer. There is a need for increased clinical and scientific collaboration and the development of guidelines for the use of emerging treatment strategies. The interactive meeting sessions fostered unique opportunities for academic debate and nurtured collaboration between the attendees.

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