Predicting the evolution of clinical skin aging in a multi-ethnic population: Developing causal Bayesian networks using dermatological expertise.

INTRODUCTION: Software to predict the impact of aging on physical appearance is increasingly popular. But it does not consider the complex interplay of factors that contribute to skin aging. OBJECTIVES: To predict the +15-year progression of clinical signs of skin aging by developing Causal Bayesian Belief Networks (CBBNs) using expert knowledge from dermatologists. MATERIAL AND METHODS: Structures and conditional probability distributions were elicited worldwide from dermatologists with experience of at least 15 years in aesthetics. CBBN models were built for all phototypes and for ages ranging from 18 to 65 years, focusing on wrinkles, pigmentary heterogeneity and facial ptosis. Models were also evaluated by a group of independent dermatologists ensuring the quality of prediction of the cumulative effects of extrinsic and intrinsic skin aging factors, especially the distribution of scores for clinical signs 15 years after the initial assessment. RESULTS: For easiness, only models on African skins are presented in this paper. The forehead wrinkle evolution model has been detailed. Specific atlas and extrinsic factors of facial aging were used for this skin type. But the prediction method has been validated for all phototypes, and for all clinical signs of facial aging. CONCLUSION: This method proposes a skin aging model that predicts the aging process for each clinical sign, considering endogenous and exogenous factors. It simulates aging curves according to lifestyle. It can be used as a preventive tool and could be coupled with a generative AI algorithm to visualize aging and, potentially, other skin conditions, using appropriate images.

Early-onset atopic dermatitis in children: which are the phenotypes at risk of asthma? Results from the ORCA cohort.

BACKGROUND: Atopic dermatitis (AD) is known to predate asthma and other atopic disorders described under the term "atopic march". However, this classic sequence is not always present and only a few studies have addressed children at risk of developing asthma. The objective of this study is to define early-onset AD phenotypes leading to asthma. METHODS: We performed a cluster analysis with 9 variables of 214 infants with early-onset AD prospectively enrolled in the ORCA cohort and followed each year on the occurrence of asthma until the age of 6. RESULTS: We identified 3 clusters - cluster 1 (n = 94) with low to no sensitization to food (27.7%) or aeroallergens (10.6%) and moderate AD severity (SCORAD 25.29 +/- 14.6) called "AD with low sensitization"; - cluster 2 (n = 84) characterized by a higher AD severity (SCORAD 32.66+/-16.6) and frequent sensitization to food (98.9%) or aeroallergens (26.2%), most likely multiple (96.4% for food allergens), called "AD with multiple sensitizations" - cluster 3 (n = 36) with parental history, moderate AD severity (SCORAD 24.46+/-15.7), moderate rate of sensitization to food allergens (38.9%) (exclusively single) with no sensitization to aeroallergens, called "AD with familial history of asthma". Percentages of children suffering from asthma at the age of 6 were higher in clusters 2 and 3 (36.1% and 33.3% respectively versus 14.9% in cluster 1, p<0.01). CONCLUSION: Two phenotypes in infants with early-onset AD convey a higher risk of developing asthma during childhood: multiple sensitization and familial history of asthma.

Natural history of allergic sensitization in infants with early-onset atopic dermatitis: results from ORCA Study.

BACKGROUND: Early-onset atopic dermatitis (AD) is a particular phenotype that may convey a risk of developing multiple sensitizations to allergens but little is known about the pathway of sensitization. The aims of this study were to describe the natural history of sensitization to allergens for this phenotype and to identify the most predictive marker associated with the risk of developing sensitization to inhaled allergens in a well-selected cohort of infants with AD. METHODS: Infants with active AD were enrolled and prospectively explored for biological markers of atopy every year until the age of 6 yr. Allergic sensitization was defined as the presence of positive specific IgEs to allergens and multiple sensitizations as being sensitized to ≥2 allergens. Elevated blood eosinophilia was defined as an eosinophil blood count ≥470 eosinophils/mm(3) and elevated total IgE as a serum IgE level ≥45 kU/l. RESULTS: Two hundred and twenty-nine infants were included. Elevated blood eosinophilia was observed at baseline in 60 children (26.2%) and elevated total IgE in 85 (37.1%). When elevated at baseline, eosinophilia and IgE levels remained significantly higher during the follow-up period. Sensitization to food allergens decreased from 58% to 34%, whereas sensitization to inhaled allergens increased over time from 17% to 67%. Initial multiple sensitizations to food allergens were the most predictive factor for the risk of developing sensitization to inhaled allergens at 6 yr (OR 3.72 [1.68-8.30] p < 0.001). CONCLUSIONS: In the early-onset AD phenotype, multiple sensitization to food allergens conveys a higher risk of sensitization to inhaled allergens than single sensitization.