Machine Learning–Based Deep Phenotyping of Atopic Dermatitis: Severity-Associated Factors in Adolescent and Adult Patients

Key Points

Question  What are the main factors associated with the severity of atopic dermatitis (AD)?

Findings  In this analysis of cross-sectional data collected from 367 patients with active AD (age ≥12 years), the most important factors associated with increased probability of severe AD were age between 12 and 21 years or older than 52 years, age at AD onset older than 12 years, total serum immunoglobulin E level greater than 1708 IU/mL, eosinophil values greater than 6.8%, atopic stigmata (cheilitis, white dermographism), male sex, sports less than once per week, (former) smoking, and alopecia areata.

Meaning  The phenotypic characteristics and patient age frames found in this cross-sectional analysis might contribute to a deeper disease understanding, estimation of severity probability, closer monitoring of predisposed patients, and personalized prevention and therapy.

Importance  Atopic dermatitis (AD) is the most common chronic inflammatory skin disease and is driven by a complex pathophysiology underlying highly heterogeneous phenotypes. Current advances in precision medicine emphasize the need for stratification.

Objective  To perform deep phenotyping and identification of severity-associated factors in adolescent and adult patients with AD.

Design, Setting, and Participants  Cross-sectional data from the baseline visit of a prospective longitudinal study investigating the phenotype among inpatients and outpatients with AD from the Department of Dermatology and Allergy of the University Hospital Bonn enrolled between November 2016 and February 2020.

Main Outcomes and Measures  Patients were stratified by severity groups using the Eczema Area and Severity Index (EASI). The associations of 130 factors with AD severity were analyzed applying a machine learning–gradient boosting approach with cross-validation–based tuning as well as multinomial logistic regression.

Results  A total of 367 patients (157 male [42.8%]; mean [SD] age, 39 [17] years; 94% adults) were analyzed. Among the participants, 177 (48.2%) had mild disease (EASI ≤7), 120 (32.7%) had moderate disease (EASI >7 and ≤ 21), and 70 (19.1%) had severe disease (EASI >21). Atopic stigmata (cheilitis: odds ratio [OR], 8.10; 95% CI, 3.35-10.59; white dermographism: OR, 4.42; 95% CI, 1.68-11.64; Hertoghe sign: OR, 2.75; 95% CI, 1.27-5.93; nipple eczema: OR, 4.97; 95% CI, 1.56-15.78) was associated with increased probability of severe AD, while female sex was associated with reduced probability (OR, 0.30; 95% CI, 0.13-0.66). The probability of severe AD was associated with total serum immunoglobulin E levels greater than 1708 IU/mL and eosinophil values greater than 6.8%. Patients aged 12 to 21 years or older than 52 years had an elevated probability of severe AD; patients aged 22 to 51 years had an elevated probability of mild AD. Age at AD onset older than 12 years was associated with increased probability of severe AD up to a peak at 30 years; age at onset older than 33 years was associated with moderate to severe AD; and childhood onset was associated with mild AD (peak, 7 years). Lifestyle factors associated with severe AD were physical activity less than once per week and (former) smoking. Alopecia areata was associated with moderate (OR, 5.23; 95% CI, 1.53-17.88) and severe (OR, 4.67; 95% CI, 1.01-21.56) AD. Predictive performance of machine learning–gradient boosting vs multinomial logistic regression differed only slightly (mean multiclass area under the curve value: 0.71 [95% CI, 0.69-0.72] vs 0.68 [0.66-0.70], respectively).

Conclusions and Relevance  The associations found in this cross-sectional study among patients with AD might contribute to a deeper disease understanding, closer monitoring of predisposed patients, and personalized prevention and therapy.

Atopic dermatitis (AD) is the most common chronic inflammatory skin disease with a high impact on patients’ quality of life, productivity at work, and burden on the health system, with an increasing cumulative lifetime prevalence of 15% to 28% in industrial nations.^1,2 The pathophysiology is complex and multifactorial. Main mechanistic factors are an altered skin barrier and a modified innate and adaptive immune system, both with a strong genetic background and influenced by gene–environment interactions as well as skin microbiome dysbiosis.^1,3-6

There is a wide range in the clinical phenotype, with severity ranging from minimal to erythrodermic forms involving the whole body and many different disease courses.^1,7-14 Apparently, the different genetic, immunologic, and environmental factors do not contribute in the same extent to every patient. This requires a more detailed definition of the heterogeneous (endo)phenotypes.

Owing to recent efforts of a precision medicine approach in AD, there has been a growing interest in deep phenotyping. Early phenotyping focused primarily on case/cohort ascertainment.^15-17 Conversely, deep phenotyping shifted the focus from identification to characterization. It aims to deliver precise and comprehensive characterization of observable traits representing unique morphological, biochemical, physiological, or behavioral properties of the identified patient populations.^16,17 This study aimed to evaluate the phenotype and potential risk factors in adolescent and adult patients with AD stratified by disease severity.

In this study, cross-sectional data from the baseline visit of a prospective longitudinal study investigating the phenotype of patients with AD were analyzed. Inpatients and outpatients 12 years and older with active AD, fulfilling the Hanifin and Rajka criteria, were enrolled between November 2016 and February 2020 in the CK-CARE program¹⁸ at the University Hospital Bonn after providing oral and written informed consent. Exclusion criteria were systemic treatment and disease remission.

All participants completed a standardized questionnaire regarding personal and family history of atopy, disease course, comorbidities, lifestyle, environment, drug intake, and the Dermatology Life Quality Index (DLQI).¹⁹ They were examined regarding severity of eczema (Eczema Area and Severity Index [EASI]),²⁰ SCORing Atopic Dermatitis (SCORAD),^21,22 and atopic stigmata by an experienced dermatologist. Patients with an EASI of 7 or less were considered as having mild disease; EASI greater than 7 and less than or equal to 21 was moderate disease; and EASI greater than 21 was severe disease.^20-22 All study methods followed the Declaration of Helsinki and have been approved by the local ethics committee.

The associations of 130 factors with AD severity were analyzed using a machine learning–gradient boosting approach with cross-validation–based tuning (MLGB)^23,24 and multinomial logistic regression with forward variable selection (MLR)²⁵ (reference group: mild AD; eMethods in the Supplement). The contribution of variables to the MLGB prediction model was evaluated by permutation-based variable importance²⁶ regarding differences in multinomial log-likelihood (eTable 1 in the Supplement). Each importance value represents the mean decrease in model fit if the values of the variable were permuted randomly. Higher numbers represent more important variables. To further illustrate the effects of each variable, we generated accumulated local effect (ALE) plots.²⁷ These show the mean change of the predictions for a given value of the respective variable compared with an average prediction. Performance of the models was evaluated by computing the predictive multiclass area under the curve (AUC)²⁸ with nested 10 × 10-fold stratified cross-validation. All P values are 2-sided and values less than .05 were considered statistically significant. Statistical analysis was conducted using R, version 3.5.3 (R Foundation)²⁹ with packages xgboost,³⁰ caret,³¹ pROC,³² coin³³ and ALEPlot³⁴ and SPSS Statistics for Windows, version 25.0 (IBM Corp).

A total of 367 patients were included: 210 (57%) female, 157 (43%) male; mean (SD) age, 39 (17) years (range, 12-89 years); 94% adults (Table; eTable 2 in the Supplement). The severity scores EASI, affected body surface area (BSA), and SCORAD highly correlated among each other (EASI vs BSA: rs = 0.82; 95% CI, 0.77-0.85; EASI vs SCORAD: rs = 0.78; 95% CI, 0.74-0.82; EASI vs objective SCORAD [oSCORAD]: rs = 0.80; 95% CI, 0.76-0.83; all P < .001).

Using the EASI score, 48.2% had mild AD (n = 177), 32.7% had moderate AD (n = 120), and 19.1% had severe AD (n = 70). The DLQI correlated significantly with severity (EASI: rs = 0.49; 95% CI, 0.41-0.57; SCORAD: rs = 0.58; 95% CI, 0.51-0.65; oSCORAD: rs = 0.52; 95% CI, 0.44-0.59; BSA rs = 0.59; 95% CI, 0.52-0.65; all P < .001). One hundred forty-two (38.9%) of all patients and greater than 50% of patients with moderate to severe AD reported a highly reduced quality of life with a DLQI greater than 11 (mild, 16.9% [n = 30]; moderate, 57.1% [n = 68]; severe, 63.8% [n = 44]).

Primary predictive factors for AD severity groups in both models were total serum immunoglobulin E (tIgE) level, age at AD onset, atopic stigmata, and sex (Table; eTable 3 in the Supplement). Eosinophil values, age, sports, and (former) smoking were further key factors in the MLGB; alopecia areata was a key factor in the MLR.

The MLGB resulted in an average multiclass AUC value of 0.71 (95% CI, 0.69-0.72), MLR regression of 0.58 (95% CI, 0.66-0.71). The more complex MLGB had a better predictive performance, but the more “classic” MLR came close (95% CI of AUC differences: 0.003-0.056).

Total serum immunoglobulin E levels and eosinophil values were important factors for predicting severity, correlated with EASI (rs = 0.43; 95% CI, 0.34-0.51 for IgE; rs = 0.24; 95% CI, 0.14-0.35 for eosinophil values [%]) and among each other (rs = 0.33; 95% CI, 0.22-0.43; all P < .001). The probability of severe AD rose strongly with tIgE levels greater than 1708 IU/mL; the probability of mild AD rose with tIgE levels less than 467 IU/mL (55 IU/mL peak probability) (Figure 1). The MLR quantified flat tIgE effects with OR of 1.16 (95% CI, 1.05-1.30) for moderate AD (P = .004) and 1.31 (95% CI, 1.16-1.46) for severe AD (P < .001) for each step of 1000 IU/mL.

A total of 263 patients (75.1%) exhibited increased tIgE levels, and 80 patients (23.0%) exhibited eosinophilia (eTable 2 in the Supplement). Patients with greater than 3.7% eosinophils had an increasing probability of moderate AD, with greater than 6.8% eosinophils associated with severe AD and with less than 3.8% (peak, 1.5%) associated with mild AD (Figure 1D).

A total of 78 patients (21%) of the cohort had an adult onset of AD, 266 had childhood onset (73%), and 22 had adolescent onset (age ≥12 years and <18 years, 6%). Atopic dermatitis severity differed between different ages at AD onset without a linear association (Figure 2). Patients with childhood onset (peak, age 7 years) had an increased probability of mild AD. Conversely, the probability of severe AD rose strongly in cases of AD onset between age 12 and 30 years with a severity peak in mid-adulthood at age 30 years, then slightly decreased, but was still elevated beyond age 30 years with a second lower peak probability at age 60 years. Mid-adulthood and late adulthood beyond age 33 years was associated with increased probability for both moderate or severe AD. In the MLR, age at AD onset was associated with moderate (OR, 1.03; 95% CI, 1.01-1.04; P = .002) and severe AD (OR, 1.04; 95% CI, 1.02-1.06 per year; P < .001). However, the trend was very flat, and the boosting model appeared to enable better description of fluctuations than the LR model, especially with early age at onset and moderate AD.

Patients aged 22 to 51 years had the highest probability of mild AD (peak, age 26 years) (eFigure 1 in the Supplement). Younger patients (age ≥12 and <19 years) had a higher probability of moderate to severe AD. Age older than 52 years was associated with increased probability of severe AD (peak, 64 years); age older than 65 years was associated with moderate or severe AD.

The so-called atopic stigmata are phenotypic traits that occur more frequently in patients with AD compared with the general population. Several atopic stigmata were associated with an increased probability for moderate to severe AD, and patients with stigmata had higher severity scores compared with patients without stigmata. In the MLR model, cheilitis sicca (OR, 8.10; 95% CI, 3.35-19.59), nipple eczema (OR, 4.97; 95% CI, 1.56-15,78), and thinning of the lateral eyebrow (Hertoghe sign) (OR, 2.75; 95% CI, 1.27-5.93) were associated with increased probability of severe AD; white dermographism was associated with moderate and severe AD (moderate: OR, 2.29; 95% CI, 1.27-4.13; severe: OR, 4.42; 95% CI, 1.68-11.64); and Dennie-Morgan fold (OR, 2.29; 95% CI, 1.27-4.13) and facial pallor/erythema (OR, 1.80; 95% CI, 1.01-3.19) were associated with moderate AD (Figure 3, Table; eFigure 2 and eTable 3 in the Supplement).

Female sex was associated with reduced probability of moderate (OR, 0.47; 95% CI, 0.26-0.84) and severe AD (OR, 0.30; 95% CI, 0.13-0.66) (Table). Male patients showed higher severity scores and tIgE levels compared with female patients (Figure 4A-C). Reported quality of life was not found to differ between male and female patients.

Regarding lifestyle factors, smokers (daily and former) had a higher probability of moderate and severe AD. Never smokers had a higher probability of mild AD and exhibited lower EASI scores compared with daily smokers (median [IQR]: 6.7 [2.4-15.7] vs 11.7 [4.0-11.4]; P = .04). Note, however, that pack-years were not recorded in the study.

Doing sports less than once per week was associated with increased probability of severe AD, while sports 1 to 3 times per week was associated with slightly increased probability of mild AD. Yet effect sizes were modest (Figure 4D; eTables 2 and 3 in the Supplement). Body mass index (calculated as weight in kilograms divided by height in meters squared) correlated with age (rs = 0.24; 95% CI, 0.14-0.33), very weakly also with tIgE level (rs = 0.16; 95% CI, 0.06-0.26) and severity (EASI rs = 0.15; 95% CI, 0.06-0.25). However, most patients with AD had normal weight (eTable 2 in the Supplement). A subgroup of adult patients with AD with cardiovascular disease (CVD) (n = 56) exhibited significantly higher body mass index (mean [SD], 27.8 [6.7] vs 25.1 [6.2]; P = .001), EASI (mean [SD], 14.7 [14.3] vs 11.2 [11.4]; P = .03) and tIgE levels (mean [SD], 5136 [10 352] vs 2617 [5655] IU/mL; P = .03) compared with AD without CVD (n = 311), but they were also older (mean [SD] age, 58.9 [15.2] vs 37.3 [15.2] years; P < .001). Regarding other nonatopic comorbidities, alopecia areata was associated with moderate (OR, 5.23; 95% CI, 1.53-17.88) and severe (OR, 4.67; 95% CI, 1.01-21.56) AD.

Atopic dermatitis is a multifactorial disease, and identification of individual risk factors still ongoing. Current advances in precision medicine emphasize the need for stratification of the highly heterogeneous phenotype. Through deep phenotyping with stratification by AD severity, we detected concrete ranges of age (and of age at AD onset), tIgE levels, eosinophil values, and atopic stigmata that might contribute to disease understanding, estimation of severity prognosis, and improved identification of suitable patients for personalized prevention and therapies at the right time point.

Male patients 12 years and older but younger than 21 years or older than 52 years, disease onset beyond 12 years, tIgE levels greater than 1708 IU/mL, eosinophil values greater than 6.8%, multiple atopic stigmata (cheilitis sicca, white dermographism, Hertoghe sign), history of (former) smoking and sports less than once per week represented the main risk candidates for severe AD in our cohort.

New key findings of our study include the detection of critical age frames. Age at AD onset older than 12 years was associated with increased probability of severe AD; onset in mid-adulthood and late adulthood beyond 33 years was associated with moderate or severe AD. Conversely, disease onset in childhood or age at time of inclusion older than 22 or younger than 51 years was associated with mild AD. This points toward different pathophysiological immunological mechanisms in childhood, adolescence, and mid-adulthood and late adulthood, potentially triggered by psychosocial factors and critical life events. However, 94% of our cohort were adults, limiting conclusions regarding adolescents.

While early birth-cohort studies have suggested a clearance of AD in more than 50% of affected children, there is growing evidence for AD as a lifelong disease with variable phenotypic expression, high rate of adult onset or relapsing AD after long asymptomatic intervals with different influencing factors depending on age (at onset), race and ethnicity, residence, and other criteria.^1,9-13,35-38

Studies reporting clinical course together with age at AD onset in adolescents and adults are scarce.³⁹ No significant differences in AD severity were found in a US cohort between age at AD onset younger than 18 years or 18 years and older.⁴⁰ Interestingly, 42% of patients with moderate to severe AD in adulthood reported a worsening of AD by or after age 12 years in the time period from age 12 to 19 years.³⁹ Our data suggest adolescence and mid-adulthood as critical time frames for the onset of AD with severe disease, while childhood onset was associated with a milder course.

In childhood, boys were reported to have a higher prevalence of allergic rhinitis and asthma^41,42 than girls, but not of AD.⁴¹ In most studies, sex-balanced AD prevalence shifts to female predominance in adulthood.^12,41 Pediatric studies have reported higher likelihood of girls for mild intermittent AD⁴³; others have linked male sex to better AD control³⁷ and to an early-onset, early-resolving course.⁹ There is evidence for suppression of type 2 response by testosterone and enhancement by estrogen and progesterone.⁴⁴ Yet IgE levels generally remain higher or comparable in men compared with women after puberty.^45-47 Higher severity scores and tIgE levels have been described in male patients with AD aged 12 to 89 years,⁷ according to our findings, indicating a more severe disease at least in adolescent and adult men with active AD. This might be a bystander phenomenon of the higher tIgE levels found in male individuals. Yet male individuals with mild disease might be less likely to participate in epidemiological studies compared with female individuals, causing a selection bias. Furthermore, the association of male sex with severity may not be generalized to children owing to an underrepresentation of the early-onset, early-resolving male course in our study collective with age 12 years and older and excluding patients in remission.

Levels of tIgE and eosinophil values are key mediators for allergic sensitization and inflammation with high evidence for severity correlation in AD.^48-50 Thus, they were expected predictive factors for severity, but we detected new scalable ranges. High tIgE levels greater than 1708 IU/mL and eosinophil values greater than 6.8% were associated with exponential rising probability of severe AD; lower tIgE levels (peak probability, 55 IU/mL) and eosinophil values less than 3.7% (peak, 1.5%) were associated with mild AD.

Active smoking and passive smoke exposure have been associated with an increased prevalence of AD.⁵¹ Active smoking has also been associated with adult onset of AD² and self-reported severe AD.⁵² We could confirm this association in physician-assessed AD severity. Potential causes are biological detrimental effects of smoke and reactive oxygen species on the immune system⁵³ and skin barrier^51,54,55 and/or worse health behavior owing to higher psychological burden⁶ in severe AD.

Atopic dermatitis has been associated with less physical activity in US⁵⁶ but not in Swedish⁵⁷ adults. We found an association of severe AD with sports less than once per week and mild AD with sports 1 to 3 times per week. Sport-avoidance in severe AD might be caused by increased pruritus⁶ or even exacerbations⁵⁸ sweating from activity,^6,59 localization of eczema,⁵⁶ and indirect effects of comorbid fatigue, sleep disturbances, and depression.^6,56 Interestingly, moderate-intensity aerobic exercise attenuated dermatitis and allergic inflammation in a mouse model for eczema.⁶⁰

Other known risk factors for AD are a positive family history for atopic diseases (especially AD), living in an urban setting and in regions with low UV light exposure or dry climatic conditions, a “Westernized” diet (high in sugars and polyunsaturated fatty acids), and repeated exposure to antibiotics before age 5 years.^{1,4,6,11,14,61} Studies vary regarding the effects of cesarean delivery, breastfeeding, maternal tobacco exposure, childhood vaccinations, bacterial or viral infections, air pollutants, farm environment, pets, sex, family size, household education level, and association with atopic and nonatopic comorbidities such as CVD, obesity, metabolic diseases, inflammatory bowel disease, rheumatoid arthritis, and psychiatric diseases.^{1,4,9,11,35-37,61-63} The variation of reported effects dependent on the study design and readout stress the need for stratification of AD.^1,2

Strengths of our study include a well-characterized cohort covering the whole spectrum of AD severity, based on dermatologist-assessed validated severity scores, assessment of multiple clinical and epidemiological characteristics, and a wide age range from early adolescence until late adulthood. Limitations include a recall bias in self-reported age at onset, particularly in older persons; only 6% adolescents in our cohort; and a potential selection bias in a specialized dermatologic setting, which may limit overall generalizability. Differentiation between pediatric and adult cohorts and stratification by AD severity, onset or persistence, and survey-based vs clinician-assessed is crucial for the interpretation of our data in the context of the literature. In contrast to other studies, such as birth-cohort studies investigating onset and persistence of AD compared with participants without AD, we did not include patients in remission (hereby also outgrown/early-resolving courses). Here, we aimed to evaluate the association of several variables with AD severity within the broad spectrum of already-existing and active AD in adolescent and adult patients to identify at-risk patients for further monitoring or therapy.

With this readout, our data further corroborate an association of maternal asthma, current and former smoking, and viral infections (herpes simplex virus, eczema herpeticum)^1,2,64 with severe AD (eTable 2 in the Supplement), although these variables only had a modest effect size on our model compared with stronger effects of tIgE levels, eosinophil values, age (at onset), or atopic stigmata. We did not observe clear effects of family size, household education level, specific diet, cesarean delivery, contact with farm animals or pets, or urban vs rural living on AD severity in our study population. Although the number of residents showed a weak negative correlation with EASI, there was no clear (linear) effect on AD severity, suggesting a cluster effect.

Alopecia areata was associated with AD severity, confirming recent other reports.^65-67 Regarding other nonatopic comorbidities, CVD was very weakly associated with AD severity, but not others, such as chronic inflammatory bowel disease, diabetes mellitus, rheumatoid arthritis, multiple sclerosis, or malignant neoplasms. Despite modest effect size, this is of interest because of the current discussion of AD as a systemic disease (CVD), effect of dupilumab treatment for alopecia areata,^66,67 and of JAK inhibitors being in clinical trials not only for AD but also for alopecia areata as promising new indication with some shared pathophysiological mechanisms.^1,67,68

A phenotype with several atopic stigmata was associated with an increased probability for moderate to severe AD, and patients with stigmata had significantly higher severity scores compared with patients without stigmata. These data complement other studies showing the association of selected atopic stigmata with AD persistence⁴ or childhood vs adult onset of AD.¹¹

A better characterization of the phenotype might help to identify patients at risk for severe disease and facilitate early therapeutic intervention and prevention. Sports and avoidance of smoking are simple self-dependent preventive lifestyle factors. Moreover, we could identify concrete critical time frames regarding age and age at onset, cutoff points for tIgE levels and eosinophil values as predictive biomarkers, and several atopic stigmata associated with disease severity in this study. Limitations of sex and a single atopic stigma as isolated parameters are the low specificity and sensitivity. However, the overall phenotype with combination of several traits, especially with the ascertained ranges for age, age at disease onset, tIgE levels, and eosinophil values might facilitate estimation of severity prognosis. This is a highly relevant question for individual patients (and parents) and frequently asked in consultations. Thus, our findings might contribute to sensitization of physicians for predisposed patients and help shared decision-making regarding frequency of follow-up visits or initiation of further diagnostics or (systemic) therapies with simple and economic predictors.

Yet, while ranges of tIgE levels and age (at onset) were conclusive regarding their predictive value for mild or severe AD, probabilities of moderate AD showed more fluctuations and less clear cutoff points. These data suggest the wide range of moderate AD defined by EASI greater than 7 and less than 21 as a black box with multiple subgroups. A reevaluation of these threshold points might help to further elucidate this picture and allow better fine-tuning of subgroups. Further stratification of the heterogeneous phenotypes by several criteria is needed for better identification of critical life periods, biomarkers, and of the right patient for personalized therapeutic and preventive measures of (severe) AD.

Accepted for Publication: July 28, 2021.

Published Online: November 10, 2021. doi:10.1001/jamadermatol.2021.3668

Corresponding Author: Laura Maintz, MD, Department of Dermatology and Allergy, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany (laura.maintz@ukbonn.de).

The CK-CARE Study Group Authors: Peter Schmid-Grendelmeier, MD; Claudia Traidl-Hoffmann, MD; Cezmi Akdis, PhD; Roger Lauener, MD; Marie-Charlotte Brüggen, MD, PhD; Claudio Rhyner, PhD; Eugen Bersuch, MD; Ellen Renner, MD; Matthias Reiger, PhD; Anita Dreher, MSc; Gertrud Hammel, MA; Daria Luschkova, MD; Claudia Lang, MD.

Affiliations of The CK-CARE Study Group Authors: Christine Kühne-Center for Allergy Research and Education Davos (CK-CARE), Davos, Switzerland (Schmid-Grendelmeier, Traidl-Hoffmann, Akdis, Lauener, Brüggen, Rhyner, Reiger, Dreher, Hammel, Luschkova); Allergy Unit, Department of Dermatology, University Hospital of Zürich, Zürich, Switzerland (Schmid-Grendelmeier, Brüggen, Bersuch, Lang); Department of Environmental Medicine, Faculty of Medicine, University of Augsburg, Augsburg, Germany (Traidl-Hoffmann, Reiger, Hammel, Luschkova); Institute of Environmental Medicine, Helmholtz Zentrum Muenchen, Augsburg, Germany (Traidl-Hoffmann, Reiger, Hammel, Luschkova); Swiss Institute of Allergy and Asthma Research (SIAF), Davos, Switzerland (Akdis); Children’s Hospital of Eastern Switzerland, St Gallen, Switzerland (Lauener); Faculty of Medicine, University of Zurich, Zürich, Switzerland (Brüggen); Translational Immunology in Environmental Medicine, School of Medicine, Technical University of Munich, Munich, Germany (Renner); Hochgebirgsklinik Davos, Davos, Switzerland (Renner).

Author Contributions: Dr Maintz had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Maintz, Welchowski, Herrmann, Bieber, Traidl-Hoffmann, Akdis, Lauener, Brüggen, Rhyner, Renner, Reiger, Luschkova.

Acquisition, analysis, or interpretation of data: Maintz, Welchowski, Herrmann, Klaeschen, Brauer, Fimmers, Schmid, Bieber, Schmid-Grendelmeier, Traidl-Hoffmann, Lauener, Bersuch, Dreher, Hammel, Lang.

Drafting of the manuscript: Maintz, Bieber.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Maintz, Welchowski, Fimmers, Schmid.

Obtained funding: Bieber, Schmid-Grendelmeier, Lauener, Brüggen, Rhyner.

Administrative, technical, or material support: Maintz, Herrmann, Klaeschen, Brauer, Bieber, Traidl-Hoffmann, Lauener, Bersuch, Renner, Dreher, Hammel.

Supervision: Maintz, Welchowski, Bieber, Traidl-Hoffmann, Akdis, Lauener, Renner, Lang.

Conflict of Interest Disclosures: Dr Maintz reported grants and personal fees from CK-CARE during the conduct of the study; and being an investigator for LEO Pharma, AbbVie, Galderma, Pfizer, and Eli Lilly outside the submitted work. Dr Herrmann reported grants and personal fees from CK-CARE during the conduct of the study. Mrs Brauer reported personal fees from CK-CARE during the conduct of the study. Drs Schmid, Hammel, and Lang reported grants from Christine Kühne-Center for Allergy Research and Education (CK-CARE) during the conduct of the study. Prof Bieber reported personal fees from LEO during the conduct of the study; personal fees from Allmiral, AnaptysBio, Arena, Asana Biosciences, Bayer Health, Boehringer Ingelheim, BMS, Domain Therapeutics, Galapagos/Morphosys, Galderma, Glenmark, Incyte, IQVIA, Jansen, Kymab, LG Chem, Lilly, Novartis, Vifor Pharma, Pfizer, Pierre Fabre, and UCB outside the submitted work; and being the founder of the nonprofit biotech company Davos Biosciences within the International Kühne-Foundation. Prof Schmid-Grendelmeier reported honoraria from AbbVie, GlaxoSmithKline, Lilly, LEO, Pfizer, Sanofi, and Novartis outside the submitted work. Prof Lauener reported grants from Kühne Foundation during the conduct of the study. Dr Rhyner reported grants from LEO Pharma during the conduct of the study. Dr Reiger reported grants from CLR, Germany, and Beiersdorf, Germany; and personal fees from Bencard, Germany; La Roche-Posay, Germany; Galderma, Germany; Sebapharma, Germany; and LEO Pharma, Germany, outside the submitted work. No other disclosures were reported.

Funding/Support: We thank the Christine Kühne-Center for Allergy Research and Education (CK-CARE), Davos, Switzerland, for funding of the study and for support of Laura Maintz, MD, Thomas Welchowski, PhD, and Juliette Brauer, BA.

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: We are extremely grateful to all our study participants, to Nina Evertz, MD, Lisa Braun, MD, and Veronika Ralser-Isselstein, MD, for their help in their recruitment and Sylvia Schnautz, Simone Willms, Helene Kirins, and Jolien Oosterveer, MSc, for excellent technical assistance. We highly appreciate the good cooperation with Beate Rückert, who contributed to the CK-CARE biobank and all other coworkers of the Prospective Longitudinal Study Investigating the Remission Phase in Patients With Atopic Dermatitis and Other Allergy-Associated Diseases Such as Asthma, Food Allergy and Allergic Rhinoconjunctivitis (ProRaD). All of these individuals were compensated for this work as members of a research staff.

Additional Information: Drs Maintz, Herrmann, and Bieber are also members of the CK-CARE Study Group.