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\begin{document}

\title{Predictive biomarker modeling of pediatric atopic dermatitis severity
based on longitudinal serum collection}



\author[1]{Sarah Engle}%
\author[1]{Ching-Yun Chang}%
\author[2]{Benjamin Ulrich}%
\author[1]{Allyson Satterwhite}%
\author[2]{Tristan  Hayes}%
\author[1]{Kim Robling}%
\author[1]{Sean Sissons}%
\author[1]{Jochen Schmitz}%
\author[2]{Rob Tepper}%
\author[2]{Mark Kaplan}%
\author[1]{Jonathan Sims}%
\affil[1]{Eli Lilly and Company}%
\affil[2]{Indiana University School of Medicine}%


\vspace{-1em}



  
  \date{June 16, 2024}


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\selectlanguage{english}
\begin{abstract}
Background: The pathogenesis of atopic dermatitis (AD) results from
complex interactions between environmental factors, barrier defects, and
immune dysregulation resulting in systemic inflammation. Therefore, we
sought to characterize circulating inflammatory profiles in pediatric AD
patients and identify potential signaling nodes which drive disease
heterogeneity and progression. Methods: We analyzed a population of 87
infants that were at high risk for atopic disease based on dermatitis
diagnoses. Clinical parameters, serum, and peripheral blood mononuclear
cells (PBMCs) were collected upon entry, and at one and four years
later. Within patient serum, 126 unique analytes were measured using a
combination of multiplex platforms and ultrasensitive immunoassays.
Results: We assessed the correlation of inflammatory analytes with AD
severity (SCORAD). Key biomarkers, such as IL-13 (corr=0.47) and TARC
(corr=0.37), among other inflammatory signals, significantly correlated
with SCORAD across all timepoints in the study. Flow cytometry and
pathway analysis of these analytes implies that CD4 T cell involvement
in type 2 immune responses were enhanced at the earliest time point
(year 1) relative to the end of study collection (year 5). Importantly,
forward selection modeling identified 18 analytes in infant serum at
study entry which could be used to predict change in SCORAD four years
later. Conclusions: We have identified a pediatric AD biomarker
signature linked to disease severity which will have predictive value in
determining AD persistence in youth and provide utility in defining core
systemic inflammatory signals linked to pathogenesis of atopic disease.%
\end{abstract}%



\sloppy


\textbf{INTRODUCTION}

Atopic dermatitis (AD) is a highly complex, heterogeneous, chronic
inflammatory skin condition characterized by a pruritic, erythematous
rash whose immune pathophysiology remains unclear. While AD has been
shown to affect up to 25\% of children and 3\% of adults worldwide,
incidence rates has been increasing 2- to 3-fold over the last several
decades {[}1{]}. Although many pediatric cases are transient in nature,
AD is often the first step of the atopic march and active disease
throughout life with up to 75\% of adults with AD reporting onset during
childhood {[}2{]}. An early diagnosis of AD has been linked with the
development of additional atopic comorbidities, such as allergic
rhinitis, food allergy, and asthma {[}3{]}. This link may be the result
of similar underlying mechanisms of disease development involving the
circulating inflammatory immune milieu in AD patients since type 2
cytokines, barrier function proteins, and allergen-specific IgE
responses have been implicated in both disease onset and persistence
{[}4, 5{]}.

The comorbid nature of AD, as well as its development in early
childhood, makes it an attractive target for predictive studies of
disease development highlighting the importance of characterizing these
early stages of pathogenesis. The systemic nature of AD has only
recently been explored within younger patients demonstrating the
presence and persistence of cell populations that produce biomarkers
like IL-22, IL-17a, and IFN\selectlanguage{greek}γ, \selectlanguage{english}and strong correlations to cells capable
of producing IL-13 were shown to increase from childhood to adulthood
{[}6, 7{]}. Because AD is a complex disease involving multiple cell
types, chemokines, and cytokines, further longitudinal studies
identifying additional biomarkers and correlating them to disease
manifestations are needed to truly characterize the complex underlying
immune signals of AD pathogenesis that drive barrier disruption and
atopy development. Therefore, using high-throughput and ultrasensitive
protein quantitation methods, we evaluated 126 secreted proteins from
the serum of 87 children diagnosed with AD in their first year of life
and correlated levels of inflammatory proteins to disease progression
over the course of this 5-year study.

Our findings provide further insight into the systemic inflammatory
serum protein and cell population profile present in the circulation of
pediatric AD patients. Previous studies utilizing this cohort of
patients revealed correlations with AD to atopic and nonatopic eczema,
food allergy, and asthma {[}8, 9{]}. Several biomarkers indicative of
active type 2 immune responses were correlated with both progression of
disease and cell phenotypes which have been linked to comorbidities
associated with non-AD-like conditions such as childhood wheezing
{[}10{]}. These correlations in early life demonstrate the potential for
development of targeted therapeutics for younger patients with mild to
moderate disease. In this manuscript, we highlight key insights into the
pathology of AD, outlining the correlations with analytes previously
identified in adult moderate to severe disease and their role in disease
development even in this mild to moderate cohort of pediatric subjects.

\textbf{METHODS}

\textbf{Patients and samples}This study included a cohort of 87 American
pediatric patients recruited in infancy (Figure 1). Serum samples were
collected from children diagnosed with chronic dermatitis as previously
described {[}9{]}. All subjects or their parents/guardians provided
written consent for participation in the study. Sample collection and
subsequent use of data was approved by the IU Institutional Review
Board. Flow cytometry analysis of PBMCs collected throughout the course
of the study was previously described {[}11{]}.

\textbf{ELISA and Multiplex Immunoassays}

\emph{Olink multiplex assay}Serum samples were analyzed with the Olink
Inflammation I Proseek multiplex assay (95302) according to
manufacturer's specifications. The levels of analyte-specific DNA
amplicons for each patient were quantified by Fluidigm Biomark HD.

\emph{Luminex bead-based multiplex immunoassay}Luminex bead-based
sandwich immunoassay was used to assess serum protein levels
quantitatively and simultaneously on the Millipore Luminex 200 Bead
Reader System. Two MILLIPLEX MAP Human Cytokine/Chemokine 41-plex
Magnetic Bead Panels (HCYTOMAG-60K-PX41 and HCP2MAG-62K-PX23) were
assayed on Curiox Biosystems DA-Bead plates and plates were washed on a
DropArray LT210 washing station following the Curiox low-volume
protocol.

\emph{ELISA}For additional quantitative assessment of important analytes
conventional sandwich enzyme-linked immunoassays (ELISA) were utilized
including Periostin (DY3548B) and sST2 (DST200) from R\&D Systems.
Levels of IL-22 and TARC were measured using MesoScale Discovery
(MSD)-based sandwich ELISA. The IL-22 assay, consisting of proprietary
antibodies and recombinant protein, utilizes a Small Spot SA--MSD plate
(L45SA-1) and in-house diluents. Following incubations, wells were
washed with 1x TBST using a Bio-TEK ELx405 and analyzed on the Meso
Quick Plex SQ 120 plate reader. TARC (K151NTD) was measured according to
manufacturer's specifications. Levels of IL-13 (102732) and IL-17A
(101599) were assessed by Quanterix single molecule array (Simoa)
bead-based 2.0 assays on the Simoa HD-1 analyzer per manufacturer
protocols.

\textbf{Statistical Analyses}

\emph{Correlation analysis of AD severity}

Repeated measures correlation (R package: rmcorr) was applied to SCORAD
and log2-transformed serum protein concentration across all years to
account the dependent structure of the same patient over time for each
marker and Benjamini-Hochberg multiple comparison adjustment across
markers to report the correlation values and adjusted \emph{p} -values
{[}12{]}. Volcano plots were used to identify significant correlations
(i.e., adjusted \emph{p} -values \textless{}0.05 and repeated measures
correlation \textgreater{}0.3 or \textless{}-0.3).

\emph{Serum protein changes over time}

For each analyte, a mixed-effect model with year, gender, and race as
fixed covariates was applied to the log2-transformed analyte
concentration. Comparisons of each analyte at each time point (Y1 to Y2,
Y1 to Y5, and Y2 to Y5) were made and significance assessed using the
Tukey Multiple Comparison Test. Volcano plots were generated and used to
identify changes from Y5 to Y1 and from Y2 to Y1 (\emph{p} -values
\textless{}0.05 and fold change \textgreater{}1.5X or \textless{}-1.5X).

\emph{Prediction model of SCORAD change}

The prediction model of the change in SCORAD at Y5 to Y1 with all the
baseline (Y1) analytes concentrations was built using JMP {[}13{]}. With
forward selection, starting with null model and add the analyte most
predicted the most on dependent measure (e.g. smallest \emph{p} -values)
one by one until including analyte predictors provided no additional
predictive power. The stopping criteria is based on minimum corrected
Akaike Information Criterion (AIC) {[}14{]}. To evaluate the performance
of prediction model, we calculate the R squares and RMSE (Root Mean
Square Error) between predicted values and observed values for all
patients and RMSE. Furthermore, the cross validation on the proposed
prediction model with selected markers was applied with 100 iterations
{[}15{]}. At each iteration, the entire baseline patient set was split
into training set (75\%) and testing test (25\%). The training set data
was used to build the model and estimate model parameters. The model~was
then~used to predict the testing set data. The R squares and RMSE
between predicted values and observed values were calculated for each
iteration to assess the robustness of prediction model.

\emph{Pathway analysis}

Pathway analysis was conducted with MetaCore using Fisher's exact test
to define relevant pathways based on differentially expressed serum
protein signatures between Y5 and Y1 samples {[}16{]}.

\textbf{RESULTS}

This study sought to understand the interplay among analytes,
relationships to disease severity, and to delineate patterns of
dysregulation through the first five years of life. Serum samples were
collected from a total of 87 pediatric AD patients upon entry, one, and
four years later {[}9{]}. The mean age at study entry was 10.4 months
and the population was evenly distributed both by gender and between
Caucasian and African American ethnicities (Figure 1). Incidence of
asthma at Y5 was observed in 65\% of patients. AD severity, measured by
SCORAD, was mild to moderate at time of enrollment (range 1-28) and
maintained an average between 10.1-14.7 across the study years (Figure
1). A total of 126 serum proteins, including IgE, were analyzed across
different platforms to identify relevant connections between various
immunological markers and disease progression, using SCORAD, across each
time point of data collections. An important measure of atopy,
circulating IgE, was measured on average to be 29.6 IU/mL at year 1 (Y1;
baseline), 32.7 IU/mL at year 2 (Y2; one year after baseline), and 29.9
IU/mL at year 5 (Y5; four years after baseline). In this patient
population, IgE was only weakly correlated to SCORAD (corr=-0.22) based
on combined analysis across all timepoints of the study (data not
shown). Considering this weak correlation, we then shifted focus to
other key inflammatory serum markers possessing positive correlation
with clinical severity across all 3 visits. The strongest positive
correlation to SCORAD across all visits were CASP-8 (corr=0.48) and
IL-13 (corr=0.47) (Figure 2). Additional proteins positively correlated
with SCORAD in these pediatric AD patients that have been previously
linked to severity in adult AD included TARC (corr=0.37) and MCP-4
(corr=0.3) (Figure 2) {[}17{]}. Interestingly, we observed proteins
shown to be elevated in the circulation of adult AD patients to be
negatively correlated with SCORAD across all 3 timepoints taken in the
first 5 years of life from these pediatric AD patients, such as sCD40L
(corr=-0.3), Eotaxin-1 (corr=-0.3), and IL-7 (corr=-0.4) (Figure 2,
Supplemental Table 1) {[}17{]}.

Understanding the dramatic developmental changes that occur in early
childhood, we utilized volcano plots to visualize differentially
expressed proteins between Y1 and both Y2 and Y5 (Figure 3a-b). Proteins
which had the highest positive correlations with disease severity, such
as IL-13, CASP-8, and TARC (Figure 2a) were observed to have the highest
concentrations early at Y1 relative to Y5 (Figure 3a). We also observed
decreasing serum concentrations of Th2 markers (TARC, IL-13, and MCP-4)
from Y1 to Y5, perhaps indicative of the dramatic elevation at early
stages of life for these patients (Figure 3c, Supplemental Figure 1).
The Th22 marker, IL-22, a highly expressed cytokine in adult AD was not
observed to change over the course of the study, suggesting the absence
of a significant role for IL-22 in the first 5 years of pediatric
disease within this cohort (Supplemental Figure 1) {[}18{]}. Similarly,
many Th1/Th17 markers, including CXCL10, IL-17A, and IFN\selectlanguage{greek}γ \selectlanguage{english}shown to be
upregulated in adult AD were unchanged in our study {[}6{]}; however,
elevated serum concentration of the related marker, CXCL11, was observed
as early as Y2 (Supplemental Figure 1).

Given the correlation of inflammatory markers with SCORAD, we wanted to
determine how these serum proteins associated with each other. Thus, we
examined correlations among SCORAD-associated analytes across all time
points of the study. We observed strong interconnectivity within two
groups of analytes: 1) CD5, CASP8, IL-12b, TRANCE, and TNFRSF9 and 2)
CXCL12, CCL19, TRAIL, and IL-7 (Figure 4). Surprisingly, IL-13, which
emerged as one of the most significant correlates with SCORAD showed
only moderate associations with other protein concentrations across all
timepoints (Figure 4, Supplemental Table 1). This observation, coupled
with our finding that many inflammatory markers, such as IL-13, decrease
in concentration over the course of the study as patients age and
utilize standard of care therapies initiated further assessment of
pathway nodes connecting these markers to disease severity.

Molecular processes linked to the analytes with higher concentrations
observed at Y1 relative to Y5 were significantly associated with
pathways related to T cell activation and cytokine secretion profiles
including Immune response pathways in T cell differentiation and
cytokine secretion (Figure 3a, Figure 5a). Juxtaposed with the Y1
pathway analysis of T cell driven response mechanisms in early AD
development, Y5 pathway analysis highlights the shift from T cell driven
disease to include innate immunity, links to Langerhans and Dendritic
cells' presence in allergic dermatitis, and the rise of asthma-related
mast cell mechanisms over time (Figure 5a-b). Proteins increased in
concentration at subsequent visits (Y2 and Y5) relative to Y1 were
sCD40L, ST1A1, 4E-BP1, CXCL12, CXCL11, CCL19 and pathway analysis
connected these to innate mechanisms (Figure 3b, Figure 5b). Most
notably, a 34-fold magnitude increase was observed in the circulating
levels of sCD40L at Y5 as compared to Y1 (Figure 3a). Molecular
processes observed to be upregulated at Y2 from Y1 were also affiliated
with innate immunity (Figure 3b) and tracked with similar innate
cell-influenced mechanisms observed at Y5 from Y1 (Figure 5b). Several
of these markers overlapped between Y2 and Y5 and all markers
significantly higher concentrations in Y5 were also seen in Y2
highlighting the early onset of these mechanisms (Figure 3a-b). These
differences reiterate the idea that blood samples from pediatric AD
patients contain strong Th2-driven signals early, though levels decrease
slightly with age as Th1 and innate-linked inflammatory markers develop.

Utilizing heatmap visualizations, we next assessed the correlations of
serum protein analytes to T cell phenotypes using previously described
flow cytometry analysis of PBMCs from the same individuals demonstrating
sweeping responses for analytes shown to track with changes in T cell
development from Y1 to Y5 (Figure 5c) {[}11{]}. We observed distinct
profiles at Y1 between circulating Th2 and Treg cells and
epithelial-derived chemokines, including two proteins most significantly
upregulated at Y1 to Y5; MIP-1a, sCD40L, CCL20, and IL-8 all showed
positive correlations with Th2 populations and were unchanged or
negatively correlated with Treg populations indicating these signaling
mediators promote T-cell mediated immune responses very early in
pediatric development (Figure 5c). sCD40L showed strong correlations at
both Y1 and Y5 with Th2 cells and did not correlate with any other cell
types (Figure 5c). Th2 cytokines, IL-4 and IL-5, strongly correlated
with NKT cell populations at Y1 and Y5, respectively, and IL-9 and
PDFGa2 positively correlated with Treg levels in Y1, but not at Y5
(Figure 5c).

Notwithstanding the complexity of immune responses observed in these
samples, the longitudinal nature of this study suggested that we may be
able to identify predictive biomarkers related to the course of disease.
We utilized the forward selection modeling approach with an Akaike
information criterion (AIC) stopping rule to predict the change of
SCORAD from Y1 to Y5 using the Y1 inflammatory protein concentrations. A
biomarker panel of 18 analytes was selected in the prediction model to
predict progression of severity from infancy to Y5 (Figure 6a,
R\textsuperscript{2}=0.64, p=0.0058). To further illustrate the
robustness of prediction, a cross validation with 100 iterations on the
proposed 18 analytes predicted model resulted in the mean of the R
squares and root of mean square error (RMSE) of 0.77 and 3.5 for the 100
iteration, respectively (Figure 6b).~The highlighted serum proteins,
which could be involved in persistence or resolution of AD, include
IL-18, MMP-10, and IL-13 (Figure 6).These analyses highlight the
objective and predictive nature of correlated analytes present in the
serum of infants for subsequent disease severity.

\textbf{DISCUSSION}

Minimally invasive biomarkers, such as serum proteins, are widely sought
after for their value in defining disease states and outcomes. In
adolescent and adult AD patients, a high total serum IgE level was a
predictive factor for poor disease prognosis {[}19{]}. Interestingly, in
these pediatric patients, many inflammatory serum biomarkers
demonstrated stronger correlation to disease severity better than IgE
alone {[}20{]}. Thus, the focus of our study was to highlight key
biomarkers in early AD that correlate with disease and potentially
predict the persistence or resolution of disease.

Multiple analytes correlated strongly with pediatric AD severity and,
most significantly, known disease markers, IL-13, MCP-4, and TARC,
correlated with SCORAD across all time points from infancy to 5 years of
age, recapitulating the importance of type 2 immune response early in AD
pathogenesis. Correlations with chemokines and monocyte chemoattractant
proteins underscores the role of eosinophils and monocytes in early AD,
while TARC and IL-13 are both mediators of Th2 inflammation and
implicated in pediatric and adult AD, emphasizing that these analytes
could represent critical nodes for better understanding AD heterogeneity
and disease progression {[}7, 21, 22{]}. Although standard of care
treatments lead to a decreased concentration of these biomarkers, the
observations and changes observed in our study demonstrate the strong
relationship these markers have with the disease pathology in pediatric
patients. Importantly, this study included ultrasensitive measurements
of numerous cytokines for which quantitative assessment has only
recently been possible, including IL-13, which is implicated as a driver
of AD based on clinical responses to lebrikizumab and dupilumab, further
implicating IL-13 in disease severity even in infancy {[}5, 23, 24{]}.

Analytes described as drivers of adult AD, such as IL-22 and IL-17A
{[}7, 18{]}, did not possess strong correlations to disease severity
within these pediatric AD samples. This is likely due to the slow
increase in serum concentration over time, limiting the role of IL-22 in
pediatric AD as seen in previous reports {[}7{]}. Though levels of
IL-17A were not correlated to SCORAD and present in very low levels, as
seen in previous pediatric studies {[}25{]}, Th17-related fibroblast
markers, CCL19 and CCL20, were observed at various timepoints throughout
our study and CCL20 positively correlated with Th2 circulating cell
populations at Y1 pointing toward an intriguing immunological overlap of
inflammatory fibroblasts and T cells. Pathway analysis of proteins
upregulated in Y5 samples reveals the emergence of innate pathways over
time and multiple gene ontology results indicate that dendritic cells
(DCs) may increase with age. Increased conventional dendritic cells were
previously described as being protective for wheezing in these pediatric
eczema patients {[}10{]}, and stimulation of these conventional DCs
isolated from atopic infants produced more interferon and IL-10 than
those derived from non-atopic infants at initial visit and the one year
follow-up visit {[}26{]}. Mounting evidence demonstrates this appearance
of symptoms known as the atopic march tracks with early disruption of
immune development and can have lasting effect into adulthood {[}27{]}.
This link between increased CCL19 and DCs further indicates a role in
atopic march from AD to asthma for CCR7, the receptor for CCL19, which
can coordinate T cell, B cell, and DC responses across pathologies
{[}28{]}.

Matrix remodeling proteins (MMP-10) and chemoattractant molecules
(MCP-3, MCP-2, CXCL5) provide further insights into the complexity of
disease mechanisms significantly associated with the progression of the
pediatric form of AD. The connections of these markers to disease
severity contextualize a shift toward Th1 immunity and myeloid
involvement developing throughout the course of the pediatric AD patient
journey concomitant with dominant Th2 responses throughout. The
identification of CXCL5 and IL-5 as important disease severity
predictive biomarkers for pediatric AD highlights the innate allergic
response early in disease progression. The high levels of sCD40L that we
observed in pediatric patients aligned with prior published observations
that this protein is elevated in the blood of pediatric patients
{[}6{]}. Additionally, sCD40L concentrations correlated throughout the
study with CD4+Th2 cells which might link to the role of CD4+CD40L+
cells in the priming of other immune cell types including CD14+
monocytes and CD8+ T cells {[}29{]}. This work aids our
contextualization of a shift toward Th1 immunity and myeloid involvement
developing throughout the course of the pediatric AD patient journey
concomitant with Th2 responses presenting initially and persisting
throughout the course of disease.

The previously reported circulating protein profiles of pediatric AD
have provided robust assessment of correlation analysis at a single
timepoint {[}6{]}. Our study builds upon these important observations
via the monitoring of circulating biomarkers over a development period
of four years adding value to the concept that objective biomarkers
could be used to objectively assess disease severity, as has been
demonstrated in adult AD {[}30{]}. We observed subsets of inflammatory
proteins that correlated with each other in a manner consistent with
early disease skewing toward type 2 immune responses. Importantly,
forward selection modeling identified 18 analytes, such as IL-18, IL-13,
and MMP-10, in infant serum that could be used to predict AD severity
four years later; therefore, these analytes might represent critical
nodes to better understand AD heterogeneity and disease progression. The
measurement of markers identified in Figure 6 should provide guidance
for therapeutic intervention strategies at early stages of life for
pediatric patients suffering from AD. This model will likely have
predictive value for AD persistence past infancy and will be useful in
further defining the pathogenesis of atopic disease allowing us to begin
to see ever branching nodes delineating the complex underlying immune
regulation. This, in turn, may guide therapeutic intervention and the
prognostic value of such serum protein measurement, outlining the need
for early treatment options in pediatric patients.

Our data highlights additional questions for the field regarding the
progression of AD as children age through adolescence into adulthood. We
did not observe elevation of IL-4 in the serum of these patients, which
could be attributed to the technical limitations of assays available at
time of testing, considering that IL-13 was measured on an
ultra-sensitive platform. We also note that though MCP-4 and TARC were
moderately correlated with disease severity, they did not factor into
the predictive modeling of disease progression possibly due to these
proteins being more broadly involved in atopic mechanisms beyond AD
{[}31{]}. Future studies with focused outcomes on other atopy measures
can provide predictive insight into mechanisms of wheezing or lung
function. Correlations to SCORAD allow us to minimize potential error
that is introduced in a longitudinal study, especially in pediatrics as
maturation and growth are heavily involved in immune system development.
This cohort and data included in this manuscript enhance our knowledge
regarding the systemic presentation of AD in children and potential
biomarker profiles which aid in predication of disease persistence or
resolution.

\textbf{Abbreviations:}

AD: atopic dermatitis

AIC: Akaike information criterion

Corr: correlation

DC: dendritic cell

IL: interleukin

MCP: monocyte chemoattractant protein

MSD: mesoscale discovery

PBMC: peripheral blood mononuclear cell

TARC: thymus and activation regulated chemokine

Y1: year 1

Y2: year 2

Y5: year 5

\textbf{FIGURE LEGENDS}

\textbf{Figure 1: Sample Population Characteristics.} This study
included a cohort of 87 patients with serum sample collection at infancy
(Y1), 1-year follow-up (Y2), and 4 years later (Y5). Samples collected
from children diagnosed with dermatitis for this study have been
previously described and assessed for clinical features including asthma
{[}9{]}.

\textbf{Figure 2: Correlations to SCORAD.} (a) Repeated measurement
correlation of SCORAD to the concentration of all analytes across all
study years. (b) Log2 protein expression of serum protein concentrations
correlated to SCORAD for key analytes of interest. 1= year 1; 2= year 2;
5= year 5.

\textbf{Figure 3: Analyte Patterns in the Population.} (a, b) Volcano
plot showing serum protein change from Y1. Proteins expressed at Y1 that
decrease in Y5 or Y2 are colored blue. Proteins expressed at Y1 that
increase at Y5 or Y2 are colored red. (c) Log2 protein expression of
serum protein concentrations for key analytes of interest. *significant
change from Y1, p-value \textless{}0.05; Y1= year 1; Y2= year 2; Y5=
year 5.

\textbf{Figure 4: Correlations Among Cytokines.} (a) Heatmap
representation of cytokine correlations to one another shown with
additional proteins of interest across the course of the study. (b)
Visual representation of cytokine correlations to each other (thickness
of connecting line) and to SCORAD (size of circle) across all time
points of the study. Red color denotes correlations [?]0.35. Blue color
denotes correlations [?]0.35.

\textbf{Figure 5: Pathway analysis.} (a) Top 10 pathway maps for highly
expressed proteins at year 1 that decrease expression by year 5. The 28
specific proteins used for this analysis are outlined in Figure 3a. (b)
Top 10 pathways of most highly expressed proteins at year 5 that were
low or absent at year 1. The 6 specific proteins used for this analysis
are outlined in figure 3a. (c) Heat map correlations of cell phenotypes
previously assessed by flow cytometry {[}11{]} to serum protein analysis
from same time point.

\textbf{Figure 6: Predictive Model.} (a) Using forward modeling with
AIC, we use the listed 18 serum analytes to predict change in SCORAD
over time. The fit of these 18 analytes within this model are defined by
root mean square error (RMSE) and R square adjusted
(R\textsuperscript{2}). (b) Samples were split into either test or
training sets to assess the reproducibility of the model.

\textbf{Supplemental Figure 1: Analyte Patterns in the Population.} Log2
protein expression of serum protein concentrations for analytes of
interest. Y1= year 1; Y2= year 2; Y5= year 5.

\textbf{Supplemental Table 1: Correlations to SCORAD and Among
Cytokines} . Repeated measurement correlations of SCORAD to the
concentration of selected analytes and correlations of cytokine
correlations to one another across all study years.

REFERENCES

1. Weidinger, S., et al., \emph{Atopic dermatitis.} Nat Rev Dis Primers,
2018. \textbf{4} (1): p. 1.

2. Silverberg, J.I., \emph{Adult-Onset Atopic Dermatitis.} J Allergy
Clin Immunol Pract, 2019. \textbf{7} (1): p. 28-33.

3. Weidinger, S. and N. Novak, \emph{Atopic dermatitis.} The Lancet,
2016. \textbf{387} (10023): p. 1109-1122.

4. Spergel, J.M. and A.S. Paller, \emph{Atopic dermatitis and the atopic
march.} J Allergy Clin Immunol, 2003. \textbf{112} (6 Suppl): p.
S118-27.

5. Bieber, T., et al., \emph{Unraveling the complexity of atopic
dermatitis: The CK-CARE approach toward precision medicine.} Allergy,
2020.

6. Brunner, P.M., et al., \emph{The blood proteomic signature of
early-onset pediatric atopic dermatitis shows systemic inflammation and
is distinct from adult long-standing disease.} J Am Acad Dermatol,
2019.\textbf{81} (2): p. 510-519.

7. Czarnowicki, T., et al., \emph{Evolution of pathologic T-cell subsets
in patients with atopic dermatitis from infancy to adulthood.} J Allergy
Clin Immunol, 2020. \textbf{145} (1): p. 215-228.

8. Bohme, M., et al., \emph{Family history and risk of atopic dermatitis
in children up to 4 years.} Clin Exp Allergy, 2003. \textbf{33} : p.
1226-1231.

9. Tepper, R.S., et al., \emph{Expired nitric oxide and airway
reactivity in infants at risk for asthma.} J Allergy Clin Immunol,
2008.\textbf{122} (4): p. 760-765.

10. Yao, W., et al., \emph{Evaluation of airway reactivity and immune
characteristics as risk factors for wheezing early in life.} J Allergy
Clin Immunol, 2010. \textbf{126} (3): p. 483-8 e1.

11. Yao, W., R.S. Tepper, and M.H. Kaplan, \emph{Predisposition to the
development of IL-9-secreting T cells in atopic infants.} J Allergy Clin
Immunol, 2011. \textbf{128} (6): p. 1357-1360 e5.

12. \emph{R: A language and environment for statistical computing.}2013;
Available from: http://www.R-project.org/.

13. \emph{JMP14} . 2018 {[}cited 1989-2019; Available from:
https://support.sas.com/downloads/index.htm?fil=2.

14. Burnham, K.P. and D.R. Anderson, \emph{2002} . 2 ed. Model Selection
and Multimodel Inference: A Practical Information Theoretic Approach:
Springer-Verlag New York.

15. Stone, M., \emph{Cross-Validatory Choice and Assessment of
Statistical Predictions.} Journal of the Royal Statistical Society:
Series B (Methodological), 1974. \textbf{36} (2): p. 111-133.

16. \emph{MetaCore} . 2020; Available from:
https://clarivate.com/cortellis/solutions/early-research-intelligence-solutions/.

17. Brunner, P.M., et al., \emph{The atopic dermatitis blood signature
is characterized by increases in inflammatory and cardiovascular risk
proteins.} Scientific Reports, 2017. \textbf{7} (1): p. 8707.

18. Nograles, K.E., et al., \emph{IL-22-producing ''T22'' T cells
account for upregulated IL-22 in atopic dermatitis despite reduced
IL-17-producing TH17 T cells.} J Allergy Clin Immunol, 2009.\textbf{123}
(6): p. 1244-52 e2.

19. Kiiski, V., et al., \emph{High serum total IgE predicts poor
long-term outcome in atopic dermatitis.} Acta Derm Venereol,
2015.\textbf{95} (8): p. 943-7.

20. Hon, K.E., et al., \emph{Are age-specific high serum IgE levels
associated with worse}

\emph{symptomatology in children with atopic dermatitis?} International
Journal of Dermatology, 2007. \textbf{46}: p. 1258-1262.

21. Kataoka, Y., \emph{Thymus and activation-regulated chemokine as a
clinical biomarker in atopic dermatitis.} J Dermatol, 2014.\textbf{41}
(3): p. 221-9.

22. Taha, R.A., et al., \emph{Evidence for increased expression of
eotaxin and monocyte chemotactic protein-4 in atopic dermatitis.} J
Allergy Clin Immunol, 2000. \textbf{105} (5): p. 1002-7.

23. Guttman-Yassky, E., et al., \emph{Efficacy and Safety of
Lebrikizumab, a High-Affinity Interleukin 13 Inhibitor, in Adults With
Moderate to Severe Atopic Dermatitis: A Phase 2b Randomized Clinical
Trial.} JAMA Dermatol, 2020.

24. Bieber, T., \emph{Interleukin-13: Targeting an underestimated
cytokine in atopic dermatitis.} Allergy, 2020. \textbf{75} (1): p.
54-62.

25. Black, A., et al., \emph{Developmental regulation of Th17-cell
capacity in human neonates.} Eur J Immunol, 2012. \textbf{42} (2): p.
311-9.

26. Yao, W., et al., \emph{Altered cytokine production by dendritic
cells from infants with atopic dermatitis.} Clin Immunol,
2010.\textbf{137} (3): p. 406-14.

27. Davidson, W.F., et al., \emph{Report from the National Institute of
Allergy and Infectious Diseases workshop on ''Atopic dermatitis and the
atopic march: Mechanisms and interventions''.} J Allergy Clin Immunol,
2019. \textbf{143} (3): p. 894-913.

28. Grinnan, D., et al., \emph{Enhanced allergen-induced airway
inflammation in paucity of lymph node T cell (plt) mutant mice.} J
Allergy Clin Immunol, 2006. \textbf{118} (6): p. 1234-41.

29. Trella, E., et al., \emph{CD40 ligand-expressing recombinant
vaccinia virus promotes the generation of CD8(+) central memory T
cells.} Eur J Immunol, 2016. \textbf{46} (2): p. 420-31.

30. Thijs, J.L., et al., \emph{A panel of biomarkers for disease
severity in atopic dermatitis.} Clin Exp Allergy, 2015. \textbf{45} (3):
p. 698-701.

31. Gr\selectlanguage{ngerman}ünig, G., et al., \emph{Interleukin 13 and the evolution of asthma
therapy.} Am J Clin Exp Immunol, 2012. \textbf{1} (1): p. 20-27.

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