FIGURE LEGENDS
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].
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.
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 <0.05; Y1= year 1; Y2= year 2; Y5=
year 5.
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.
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.
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
(R2). (b) Samples were split into either test or
training sets to assess the reproducibility of the model.
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.
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., Atopic dermatitis. Nat Rev Dis Primers,
2018. 4 (1): p. 1.
2. Silverberg, J.I., Adult-Onset Atopic Dermatitis. J Allergy
Clin Immunol Pract, 2019. 7 (1): p. 28-33.
3. Weidinger, S. and N. Novak, Atopic dermatitis. The Lancet,
2016. 387 (10023): p. 1109-1122.
4. Spergel, J.M. and A.S. Paller, Atopic dermatitis and the atopic
march. J Allergy Clin Immunol, 2003. 112 (6 Suppl): p. S118-27.
5. Bieber, T., et al., Unraveling the complexity of atopic
dermatitis: The CK-CARE approach toward precision medicine. Allergy,
2020.
6. Brunner, P.M., et al., 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, 2019.81 (2): p. 510-519.
7. Czarnowicki, T., et al., Evolution of pathologic T-cell subsets
in patients with atopic dermatitis from infancy to adulthood. J Allergy
Clin Immunol, 2020. 145 (1): p. 215-228.
8. Bohme, M., et al., Family history and risk of atopic dermatitis
in children up to 4 years. Clin Exp Allergy, 2003. 33 : p.
1226-1231.
9. Tepper, R.S., et al., Expired nitric oxide and airway
reactivity in infants at risk for asthma. J Allergy Clin Immunol, 2008.122 (4): p. 760-765.
10. Yao, W., et al., Evaluation of airway reactivity and immune
characteristics as risk factors for wheezing early in life. J Allergy
Clin Immunol, 2010. 126 (3): p. 483-8 e1.
11. Yao, W., R.S. Tepper, and M.H. Kaplan, Predisposition to the
development of IL-9-secreting T cells in atopic infants. J Allergy Clin
Immunol, 2011. 128 (6): p. 1357-1360 e5.
12. R: A language and environment for statistical computing.2013; Available from: http://www.R-project.org/.
13. JMP14 . 2018 [cited 1989-2019; Available from:
https://support.sas.com/downloads/index.htm?fil=2.
14. Burnham, K.P. and D.R. Anderson, 2002 . 2 ed. Model Selection
and Multimodel Inference: A Practical Information Theoretic Approach:
Springer-Verlag New York.
15. Stone, M., Cross-Validatory Choice and Assessment of
Statistical Predictions. Journal of the Royal Statistical Society:
Series B (Methodological), 1974. 36 (2): p. 111-133.
16. MetaCore . 2020; Available from:
https://clarivate.com/cortellis/solutions/early-research-intelligence-solutions/.
17. Brunner, P.M., et al., The atopic dermatitis blood signature
is characterized by increases in inflammatory and cardiovascular risk
proteins. Scientific Reports, 2017. 7 (1): p. 8707.
18. Nograles, K.E., et al., 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, 2009.123 (6): p. 1244-52 e2.
19. Kiiski, V., et al., High serum total IgE predicts poor
long-term outcome in atopic dermatitis. Acta Derm Venereol, 2015.95 (8): p. 943-7.
20. Hon, K.E., et al., Are age-specific high serum IgE levels
associated with worse
symptomatology in children with atopic dermatitis? International
Journal of Dermatology, 2007. 46: p. 1258-1262.
21. Kataoka, Y., Thymus and activation-regulated chemokine as a
clinical biomarker in atopic dermatitis. J Dermatol, 2014.41 (3): p. 221-9.
22. Taha, R.A., et al., Evidence for increased expression of
eotaxin and monocyte chemotactic protein-4 in atopic dermatitis. J
Allergy Clin Immunol, 2000. 105 (5): p. 1002-7.
23. Guttman-Yassky, E., et al., 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., Interleukin-13: Targeting an underestimated
cytokine in atopic dermatitis. Allergy, 2020. 75 (1): p. 54-62.
25. Black, A., et al., Developmental regulation of Th17-cell
capacity in human neonates. Eur J Immunol, 2012. 42 (2): p.
311-9.
26. Yao, W., et al., Altered cytokine production by dendritic
cells from infants with atopic dermatitis. Clin Immunol, 2010.137 (3): p. 406-14.
27. Davidson, W.F., et al., 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. 143 (3): p. 894-913.
28. Grinnan, D., et al., Enhanced allergen-induced airway
inflammation in paucity of lymph node T cell (plt) mutant mice. J
Allergy Clin Immunol, 2006. 118 (6): p. 1234-41.
29. Trella, E., et al., CD40 ligand-expressing recombinant
vaccinia virus promotes the generation of CD8(+) central memory T
cells. Eur J Immunol, 2016. 46 (2): p. 420-31.
30. Thijs, J.L., et al., A panel of biomarkers for disease
severity in atopic dermatitis. Clin Exp Allergy, 2015. 45 (3):
p. 698-701.
31. Grünig, G., et al., Interleukin 13 and the evolution of asthma
therapy. Am J Clin Exp Immunol, 2012. 1 (1): p. 20-27.