Abstract

   Beyond classic “allergic”/atopic comorbidities, atopic dermatitis (AD)
   emerges as systemic disease with increased cardiovascular risk. To
   better define serum inflammatory and cardiovascular risk proteins, we
   used an OLINK high-throughput proteomic assay to analyze
   moderate-to-severe AD (n = 59) compared to psoriasis (n = 22) and
   healthy controls (n = 18). Compared to controls, 10 proteins were
   increased in serum of both diseases, including Th1 (IFN-γ, CXCL9,
   TNF-β) and Th17 (CCL20) markers. 48 proteins each were uniquely
   upregulated in AD and psoriasis. Consistent with skin expression, AD
   serum showed up-regulation of Th2 (IL-13, CCL17, eotaxin-1/CCL11,
   CCL13, CCL4, IL-10), Th1 (CXCL10, CXCL11) and Th1/Th17/Th22
   (IL-12/IL-23p40) responses. Surprisingly, some markers of
   atherosclerosis (fractalkine/CX3CL1, CCL8, M-CSF, HGF), T-cell
   development/activation (CD40L, IL-7, CCL25, IL-2RB, IL-15RA, CD6) and
   angiogenesis (VEGF-A) were significantly increased only in AD. Multiple
   inflammatory pathways showed stronger enrichment in AD than psoriasis.
   Several atherosclerosis mediators in serum (e.g. E-selectin,
   PI3/elafin, CCL7, IL-16) correlated with SCORAD, but not BMI. Also, AD
   inflammatory mediators (e.g. MMP12, IL-12/IL-23p40, CXCL9, CCL22,
   PI3/Elafin) correlated between blood and lesional as well as
   non-lesional skin. Overall, the AD blood signature was largely
   different compared to psoriasis, with dysregulation of inflammatory and
   cardiovascular risk markers, strongly supporting its systemic nature
   beyond atopic/allergic association.

Introduction

   Atopic dermatitis (AD) is the most common chronic inflammatory skin
   disease, affecting more than 30 million people in the US^[49]1. It
   often begins during early childhood, and adult patients frequently have
   chronic disease for decades^[50]2–[51]4. AD has a complex immune milieu
   with Th2 skewing, but also shows Th22, Th17, and Th1
   activation^[52]5,[53]6. In approximately one third of patients, AD is
   associated with allergic manifestations (e.g. asthma, food allergies,
   seasonal allergies)^[54]4,[55]7. Recently, associations have also been
   reported with other inflammatory diseases, including rheumatoid
   arthritis, inflammatory bowel disease^[56]8, and systemic lupus
   erythematosus^[57]9. Emerging data suggests that similar to chronic
   plaque-type psoriasis, another chronic inflammatory skin disease, AD
   patients also harbor increased cardiovascular risk factors, including
   higher rates of cardiovascular disease in some
   populations^[58]10–[59]15 that is independent of genetic risk^[60]14.
   Consistently, an increased prevalence of coronary artery calcifications
   is reported in severe AD patients compared to healthy controls^[61]16.

   However, reports on individual comorbid conditions such as stroke or
   myocardial infarction are controversial and
   inconsistent^[62]11–[63]14,[64]17, possibly dependent on contributing
   factors such as obesity^[65]15,[66]18,[67]19.

   It is currently established that chronic inflammation accelerates
   atherosclerosis due to repetitive vascular injury^[68]20. For example,
   elevated serum levels of TNF-α and IL-17 are thought to contribute to
   increased cardiovascular risk in chronic plaque
   psoriasis^[69]21,[70]22, possibly mediating endothelial damage. In
   vitro data suggests that IL-17 can indeed contribute to
   pro-inflammatory changes in endothelial cells, and inhibition of IL-17
   in a mouse model of atherosclerosis showed significantly ameliorated
   disease^[71]23,[72]24. Importantly, many of these “psoriasis” mediators
   are also found in AD^[73]5,[74]25,[75]26. Like AD, allergic asthma is a
   Th2-driven disease with contributions of additional cytokine
   pathways^[76]27. Patients with asthma are demonstrated to be at
   increased risk for atherosclerosis^[77]28, implying that chronic
   inflammation across diseases in general, rather than a specific
   cytokine, may be primarily responsible for increased cardiovascular
   risk^[78]29.

   Increasing evidence exists for systemic immune activation in AD.
   Several flow cytometry studies showed increases in activated T-cell
   subsets in blood from moderate-to-severe AD patients as compared to
   controls, or psoriasis patients of comparable disease
   severity^[79]25,[80]30. Furthermore, several serum biomarkers have been
   recently reported to correlate with baseline AD severity and/or
   therapeutic responses. These include Th2 measures such as IL-13, IL-31,
   CCL17, CCL22, CCL13, Th22-related products (IL-22), Th1-related markers
   (CXCL10, IFN-γ), E-selectin, IL-16, IL-18, IgE, and eosinophil cationic
   protein (ECP)^[81]31–[82]38. Moreover, non-lesional biomarkers show
   even higher correlations with disease severity compared to lesional
   biomarkers^[83]33, also suggesting the systemic nature of AD rather
   than a disease with only lesional skin-focused inflammation^[84]39. To
   characterize a potential systemic inflammatory and cardiovascular risk
   signature in AD, we assessed respective serum markers using a high
   throughput proteomic platform. We screened for a large panel of
   established and exploratory inflammation and cardiovascular risk
   biomarkers^[85]40 in serum of moderate-to-severe AD patients with an
   OLINK Proseek® Multiplex assay that uses proximity extension assay
   (PEA) technology^[86]41,[87]42, in comparison to matched controls and
   moderate-to-severe psoriasis patients. We chose psoriasis as a
   comparator as it serves as a positive control for systemic
   inflammation^[88]21. We show that moderate-to-severe AD patients have
   increases in multiple inflammatory and cardiovascular risk proteins in
   serum, that are largely different from increases in blood from
   psoriasis patients.

Results

The proteomic blood signature of AD is largely different from psoriasis

   Using the OLINK high-throughput proteomic platform, we assessed a panel
   of 257 immunological and cardiovascular risk proteins in serum of
   moderate-to-severe AD and psoriasis patients in comparison to healthy
   control subjects (Table [89]1). The proximity extension technology used
   in our study is potentially superior to conventional multiplex
   immunoassays, since only correctly matched antibody pairs give a
   signal, yielding higher specificity and sensitivity^[90]40,[91]41. The
   body-mass-index (BMI) was similar in AD and controls, while higher in
   psoriasis patients. Among the 257 investigated proteins, only 11 were
   significantly upregulated in both diseases when compared to controls
   (Fig. [92]1a). The majority of markers were exclusively upregulated in
   either AD (n = 45) or psoriasis (n = 53), and only a minority were
   downregulated (Fig. [93]1a, Supplementary Table [94]S1). When results
   were adjusted for BMI (Fig. [95]1b, Supplementary Table [96]S2) and for
   the presence of asthma and the cardiovascular risk factors such as
   arterial hypertension, diabetes mellitus, and hypercholesterolemia
   (Supplementary Figure [97]S1, Supplementary Table [98]S3), the observed
   differences between AD and psoriasis, compared to controls, were
   largely preserved. Mutually upregulated proteins included markers of
   Th1 (IFN-γ, CXCL9, TNF-β/lymphotoxin) and Th17 (CCL20, IL-17C) immune
   responses, the CD4 T-cell chemoattractant IL-16, and the
   differentiation/proliferation factor IL-20 (Fig. [99]1c).

Table 1.

   Summary of demographics and disease severity of study subjects.
   AD Healthy Psoriasis p-value
   Sample size n = 59 n = 18 n = 22
   Age (y) (mean, SD) 40.5 (15.2) 41.3 (10.3) 46.8 (11.4) 0.208
   BMI (mean, SD) 27.7 (6.1) 27.6 (4.4) 31.5 (5.2) *0.031
   SCORAD (mean, SD) 54.1 (13.2) NA NA
   PASI (mean, SD) NA NA 27.6 (9.9)
   Gender *0.042
   Female (n, %) 28 (47.5%) 6 (33.3%) 4 (18.2%)
   Male (n, %) 31 (52.5%) 12 (66.7%) 18 (81.8%)
   Ethnicity *** < 0.001
   Asian (n, %) 15 (25.4%) 3 (16.7%) 2 (9.1%)
   African American (n, %) 24 (40.7%) 9 (50.0%) 0 (0.0%)
   Caucasian (n, %) 20 (33.9%) 6 (33.3%) 16 (72.7%)
   Serum IgE (kU/L) (median, IQR) 2,412 (5,028) NA NA NA
   Eosinophils (%) (mean, SD) 6.91 (4.94) NA NA NA
   Asthma bronchiale ** < 0.001
   No 39 (66.1%) 18 (100.0%) 21 (95.5%)
   Yes 20 (33.9%) 0 (0.0%) 1 (4.5%)
   Arterial hypertension (AT) 0.759
   No 51 (86.4%) 17 (94.4%) 19 (86.4%)
   Yes 8 (13.6%) 1 (5.6%) 3 (13.6%)
   Diabetes mellitus (DM) 0.475
   No 55 (93.2%) 18 (100.0%) 22 (100%)
   Yes 4 (6.8%) 0 (0.0%) 0 (0.0%)
   Hypercholesterolemia (HCh) 0.329
   No 52 (88.1%) 18 (100.0%) 19 (86.4%)
   Yes 7 (11.9%) 0 (0.0%) 3 (13.6%)
   Cardiovascular disease risk factor present (AT and/or DM and/or HCh)
   0.439
   No 48 (81.4%) 17 (94.4%) 18 (81.8%)
   Yes 11 (18.6%) 1 (5.6%) 4 (18.2%)
   [100]Open in a new tab

   One-way ANOVA was used for comparisons of means, and Fisher’s exact
   test was used for comparisons of proportions; *p < 0.05, **p < 0.01,
   ***p < 0.001; y years, SD standard deviation, IQR interquartile range.

Figure 1.

   [101]Figure 1
   [102]Open in a new tab

   Regulation of inflammatory and cardiovascular risk proteins in AD and
   psoriasis vs. healthy controls. Venn diagrams of regulated serum
   proteins compared to healthy controls, adjusted for age/gender (a) and
   age/gender/BMI (b). Markers that were significantly upregulated
   (FCH > 1.3, FDR < 0.1) in both AD and psoriasis (c), only in AD (d) or
   only in psoriasis (e) are depicted as log2 fold change over healthy
   control serum with their 95% confidence intervals.

   AD, but not psoriasis, was characterized by significant upregulation of
   several additional Th1 (CXCL10, CXCL11), Th2 (IL-13, CCL13, CCL17,
   CCL11, IL-10), Th17/Th22 (S100A12) and Th1/Th17/Th22 (IL-12/IL-23p40)
   associated products, as well as a broad array of proteins involved in
   the development and activation of T-cells (CD40L, IL-7, CCL25, IL2RB,
   IL15RA, CD6, RANKL, TNFRSF9/CD137) and dendritic cells/DC (CD40, FLT3
   ligand) (Fig. [103]1d). Mediators involved in atherosclerosis
   (CX3CL1/fractalkine, CCL8, M-CSF, CXCL5, CCL4, HGF), tissue remodeling
   (MMP-12, MMP-1, MMP-10) and angiogenesis (VEGF-A) were also increased
   in AD. The growth factor TGF-α, the NF-κB-activator IKBKG/NEMO,
   molecules mediating chemotaxis of T-cells/B-cells/eosinophils (CCL28)
   and neutrophils (CXCL5, CXCL6), as well as the soluble cytokine
   receptors IL-10RB and IL-18R1 were also higher in AD (Fig. [104]1d).

   Proteins significantly upregulated only in psoriasis included “classic”
   psoriasis markers such as Th17-associated products PI3/Elafin and
   IL-17A^[105]43, as well as the angiopoietin receptor
   (Tie2)^[106]44,[107]45 (Fig. [108]1e). Significant increases were noted
   for molecules involved in coagulation and angiogenesis (t-PA),
   endothelial activation (IL-6, E-selectin, CCL2), lipid metabolism
   (FABP4, PCSK9, LDL-R), as well as the adipokine resistin
   (Fig. [109]1e). Leptin, which reflects total body adipose
   tissue^[110]46, was increased in the BMI-uncorrected cohort
   (Supplementary Table [111]S1), but was no longer increased in psoriasis
   after correction for BMI (Fig. [112]1e, Supplementary Table [113]S2).
   The IL-1 inhibitor IL-1RA, the macrophage marker CD163 (a
   hemoglobin-haptoglobin scavenger), several cytokines and soluble
   cytokine receptors (CCL2, CCL24, TRAIL, TRAIL-R1, TNF-R1, TNF-R2), the
   IL-18 inhibitor IL-18BP, the angiogenesis inhibitor GDF-2,
   cardiovascular disease risk proteins (galectin-3, cathepsin L1,
   cathepsin D, IGFBP2), as well as mediators of cell adhesion (integrin
   β2, SLAMF7), apoptosis (FAS, Caspase-8), T cell activation (TREML2),
   proliferation (AXL), co-stimulation (PD-L2), and humoral immunity
   (TNFRSF13B) were also increased only in psoriasis. In sum, the majority
   of significantly regulated proteins were different between AD and
   psoriasis.

Correlation with skin disease suggests skin-blood-interaction

   To investigate whether the extent of AD skin disease influences levels
   of blood markers, we correlated serum proteins with the clinical AD
   measures SCORAD (SCORing Atopic Dermatitis, Fig. [114]2a) and body
   surface area (BSA) involvement (Fig. [115]2b). The endothelial cell
   activation marker E-selectin, the Th17 marker PI3/Elafin, the
   CCR1/CCR2/CCR3 chemokine CCL7, and the CD4^+ cell chemoattractant IL-16
   showed the most significant correlations for both SCORAD and BSA
   (Fig. [116]2a,b). Those serum markers were only correlated with skin
   scores, but not with body-mass index (BMI) (Fig. [117]2c–j). Leptin, a
   measure of total body fat, only correlated with BMI, but not with
   SCORAD or BSA (Fig. [118]2k–l), as expected. A complete list of
   correlation coefficients can be found in Supplementary Table [119]S4.

Figure 2.

   [120]Figure 2
   [121]Open in a new tab

   Blood protein correlations with skin disease severity (SCORAD, BSA) and
   BMI. Pearson correlation coefficients of AD serum proteins
   significantly correlated with SCORAD (a) and body surface area/BSA (b),
   and their respective scatter plots (c–j), as well as leptin (k–l), in
   comparison to correlations with body mass index/BMI.

Mutual regulation of blood and skin markers

   We next compared blood protein levels in AD with a robust meta-analysis
   derived atopic dermatitis (MADAD) skin transcriptome that integrates
   several published cohorts^[122]47. Markers that were upregulated in
   both the MADAD transcriptome and the blood were mostly chemokines, as
   depicted in Fig. [123]3.

Figure 3.

   Figure 3
   [124]Open in a new tab

   Blood-skin comparisons. Fold change comparisons of AD serum protein
   levels (AD vs. healthy controls) and skin MADAD transcriptome levels
   (lesional vs. non-lesional AD).

Correlations of blood inflammatory mediators were found with both lesional
and non-lesional AD skin

   As mostly chemokines showed concomitant regulation between lesional
   skin and serum of AD patients (Fig. [125]3), we further compared levels
   of immune markers in skin and blood of our patients. We quantified a
   panel of inflammatory molecules in respective lesional and non-lesional
   biopsies of our AD cohort using RT-PCR, and correlated them with serum
   OLINK levels (Fig. [126]4, Supplementary Figure [127]S2). We found
   significant positive correlations for the general inflammation marker
   MMP12, the Th1/Th17 cytokine IL-12/IL-23p40, as well as Th1 (CXCL9),
   Th2 (CCL22) and Th17 (PI3/Elafin) markers (Fig. [128]4). Notably, these
   correlations were consistently found not only with lesional, but also
   with non-lesional skin (Fig. [129]4). IL-17A in skin was only
   upregulated in a subset of patients (Supplementary Figure [130]S2i–j).
   When assessing skin with detectable IL-17, significant correlations of
   lesional (r = 0.53, p = 0.004), but not non-lesional skin were obtained
   with blood (r = 0.28, p = 0.29, data not shown).

Figure 4.

   [131]Figure 4
   [132]Open in a new tab

   Blood-skin correlations of inflammatory mediators. Correlation plots of
   selected serum protein levels with their corresponding lesional and
   non-lesional skin mRNA levels (a–j); scatterplots with estimated linear
   regression and 95% confidence interval; r Pearson correlation; ρ
   Spearman correlation.

Multiple inflammatory pathways show a stronger enrichment in AD than in
psoriasis

   We also conducted an enrichment analysis by using various published
   pathways (Kyoto Encyclopedia of Genes and Genomes/KEGG, Reactome
   Pathway Database, BioCarta, Pathway Interaction Database/PID) to
   compare AD and psoriasis blood proteomic profiles (Supplementary
   Table [133]S5)^[134]48–[135]52. Selected pathways significantly
   enriched in the serum of AD and/or psoriasis (in comparison to
   controls) are depicted in Fig. [136]5, with the green vertical line
   representing an FDR cutoff of 0.05. The top significantly enriched
   pathways in AD and psoriasis included Cytokine–Cytokine-Receptor
   Interaction, Chemokine–Chemokine-Receptor Binding, Chemokine Signaling,
   Cytokine Network, Jak-STAT Signaling, Toll-like Receptor Signaling, and
   IL-23 Signaling. Of these, only IL-23 signaling was more enriched in
   psoriasis, while all other pathways showed stronger enrichment in AD.
   Pathways exclusively enriched in AD included Cytokines and Inflammatory
   Responses, Th1/Th2 Differentiation, Dendritic Cell Pathway, Asthma,
   IL-12 STAT4 Signaling, CD40L Signaling, IL-4 Signaling, and CXCR3
   Signaling, IL-2 STAT5 Signaling, CD8+ T-cell Signaling, and TCR
   Signaling. Pathways exclusively enriched in psoriasis included
   HIF1-alpha Transcription Factor Network, AMB2 Neutrophils Pathway,
   NK-Cell Mediated Cytotoxicity, Ceramide Signaling, and Innate Immune
   System (Reactome).

Figure 5.

   [137]Figure 5
   [138]Open in a new tab

   Pathway analysis. Selected pathways enriched in AD and psoriasis serum,
   compared to respective healthy controls; the green line indicates false
   discovery rate/FDR < 0.05.

Discussion

   While several studies have described a limited set of biomarkers being
   increased in the blood of AD patients^[139]31–[140]33, the current
   study is the first to investigate a broad array of immune and
   cardiovascular risk proteins in serum of moderate-to-severe AD
   patients, compared to psoriasis and controls. A large bulk of evidence
   in psoriasis suggests an increase in cardiovascular risk factors and
   associated cardiovascular comorbidity^[141]53–[142]55, with similar
   data only recently emerging for AD^[143]5,[144]39. The concept of
   increased cardiovascular risk and disease is supported by several
   epidemiological cohort studies^[145]10,[146]18,[147]19,[148]56,
   case-control studies^[149]13,[150]15,[151]16 and population based
   surveys^[152]12. Using coronary computed tomography angiography, AD
   patients showed increases in coronary artery disease compared to
   healthy controls^[153]16. However, some other AD studies showed only
   marginal or no increased cardiovascular
   manifestations^[154]11,[155]14,[156]57, adding to the controversy as to
   whether AD is an independent risk factor for cardiovascular disease,
   mandating further investigation.

   Recent studies in blood of severe AD patients showed significantly
   increased T-cell activation^[157]25,[158]30 and increases in serum
   cytokines^[159]31,[160]33 that correlated with clinical severity. Our
   study is the first to define inflammatory and cardiovascular risk
   proteins that are commonly upregulated in AD and psoriasis. These
   markers are largely associated with Th1 (IFN-γ, CXCL9, TNF-β,
   lymphotoxin-α), but also Th17 (CCL20) responses. Consistently, these
   immune axes are also upregulated in chronic skin lesions of both
   diseases^[161]58. IL-20 is involved in epidermal hyperplasia and
   inhibits keratinocyte differentiation^[162]59, but is also expressed in
   atherosclerotic plaques, and was shown to promote atherosclerosis in a
   mouse model^[163]60. IL-16, a chemo-attractant for CD4^+ T-helper cells
   and myeloid cells, is also expressed in atherosclerotic plaques, but
   may have a plaque-stabilizing effect^[164]61,[165]62. These factors,
   together with an environment of chronic/Th1-triggered
   inflammation^[166]4,[167]63, with various degrees of Th17
   activation^[168]26,[169]64,[170]65, suggest a potentially
   pro-atherogenic milieu in the blood of both psoriasis and AD patients
   that may directly impact endothelial cells^[171]66.

   While we observed increases of several specific inflammatory and
   cardiovascular risk proteins only in psoriasis, including markers of
   coagulation, angiogenesis, endothelial activation and lipid metabolism,
   consistent with its well-established systemic inflammatory
   nature^[172]67–[173]69, we also observed a large set of markers
   exclusively upregulated in AD. Consistent with skin
   data^[174]4,[175]5,[176]58,[177]70, we found increases of Th1 (CXCL10,
   CXCL11), Th2 (IL-13, CCL13, CCL17, CCL11, IL-10), Th17/Th22 (S100A12)
   and Th1/Th17/Th22 (IL-12/IL-23p40) associated products in serum of AD
   patients. The fact that blood levels of the inflammatory marker MMP12
   and several mediators from all T-helper-cell axes (CXCL9-Th1,
   CCL22-Th2, PI3-Th17, IL-12p40-Th1/Th17/Th22) correlated not only with
   lesional, but also with non-lesional skin, point to the critical role
   of systemic inflammation in immune abnormalities that AD already
   harbors in non-lesional skin. This systemic inflammation consists of a
   strong adaptive component, evidenced by increases in multiple factors
   involved in T-cell development and activation, such as CD40L, IL-7,
   CCL25, IL-2RB, IL-15RA, and CD6. IL-2RB is of special interest as it is
   part of the high-affinity IL-2 receptor, which is involved in
   transduction of mitogenic signals from IL-2.

   IL-17C, produced by keratinocytes, endothelial cells, and leukocytes in
   the skin, can induce anti-microbial peptides in synergy with IL-22,
   TNF-α, and IL-1β^[178]71. In atherosclerotic plaques, smooth muscle
   cell-derived IL-17C plays a pro-atherogenic role by supporting
   recruitment of Th17 cells^[179]72. IL-17C and TNF-β, elevated in AD,
   have overlapping signaling pathways with IL-17A/F and TNF-α, which are
   thought to contribute to atherosclerosis in psoriasis^[180]21,[181]23.
   Thus, synergistic effects on endothelial cells^[182]73,[183]74 and
   other cell types need to be considered. TNFSF14/LIGHT, a
   pro-inflammatory cytokine associated with atherosclerosis^[184]40,
   plays crucial roles in T-cell homing into inflamed tissues^[185]75, and
   in the induction of matrix metalloproteinases/MMPs in
   macrophages^[186]76,[187]77. Several MMPs, which are involved in tissue
   remodeling including atherosclerosis^[188]78, were also increased in AD
   serum (MMP-1, MMP-12, MMP-10).

   When integrating many of these markers by using established lists of
   inflammatory pathways, we found enrichment of multiple pathways in AD
   to a much higher degree than in psoriasis. Enriched pathways included
   those also triggered by other atopic conditions, and particularly
   asthma (i.e. IL-4 immune signaling), also evidenced by the efficacy of
   specific Th2 targeting-strategies in both AD and
   asthma^[189]79,[190]80. Many more immune pathways showed stronger
   enrichment in AD compared to psoriasis (e.g. cytokine-cytokine receptor
   interaction, chemokine signaling pathway, cytokines and inflammatory
   response, dendritic cell pathway, Th1/Th2 differentiation). These
   findings together with our past flow cytometry studies^[191]30, support
   a stronger systemic inflammation in AD compared to psoriasis.

   Atherosclerosis is mediated by local inflammatory mediators including
   chemokines and their receptors, that are involved in the recruitment of
   inflammatory cells to the intima as an essential step in plaque
   development^[192]81. Such mediators were numerously increased in our AD
   cohort, including CCL4, CCL17, CCL28, CXCL5, CXCL10, and
   CX3CL1/fractalkine. CX3CL1/fractalkine is produced by endothelial
   cells, and is a strong chemoattractant for monocytes and lymphocytes,
   mediating their extravasation^[193]82. CCL4 and its receptor CCR5 have
   recently been demonstrated to play diverse roles in the inflammatory
   events underlying cardiovascular diseases and diabetes
   mellitus^[194]83. CXCL5 is increased in atherosclerosis, mediating a
   protective role in a mouse model by modulating macrophage
   activation^[195]84. CCL28 is chemotactic to T-cells, B-cells, and
   eosinophils to mucosal effector sites, and is increased in
   asthma^[196]85. CCL17 has been shown to drive atherosclerosis by
   restraining regulatory T-cell homeostasis^[197]86, and CXCL10 is
   associated with the severity of coronary artery disease^[198]87.

   Several growth factors associated with atherosclerosis were also
   increased, such as the vascular growth factor VEGF-A^[199]88.
   Hepatocyte growth factor/HGF, produced by mesenchymal cells, is a
   biomarker of macroangiopathy^[200]89, and circulating HGF levels have
   been positively associated with stroke^[201]90. CD137, a co-stimulatory
   molecule expressed on activated T-cells, B-cells, DCs, as well as
   endothelial cells^[202]91, increases atherosclerosis in an ApoE(-/-)
   mouse model via leukocyte recruitment and inflammation^[203]92.

   Taken together, we found multiple factors to be uniquely increased in
   AD that might be contributors of a pro-atherogenic burden in this
   disease. In line with recent
   publications^[204]10,[205]15,[206]18,[207]19 showing increases in BMI
   in North American and Asian children and adults with AD, our cohort was
   also overweight. However, inflammatory mediators involved in
   atherosclerosis development (E-selectin, CCL7, IL16, PI3/elafin) were
   significantly correlated with AD severity/SCORAD, but not with BMI,
   strongly suggesting the contribution of cutaneous disease to
   cardiovascular morbidity.

   Also, while associations with cardiovascular outcomes were reported in
   US and Asian studies^[208]10,[209]15,[210]93, only small increases in
   angina pectoris, arterial hypertension and peripheral arterial disease
   risks were found in a German cohort^[211]14. This European cohort also
   did not show increases in genetic risk factors for cardiovascular
   disease in AD^[212]14. One might speculate that varying degrees of
   cardiovascular disease across cohorts results from varying decades of
   chronic disease rather than due to shared genetic risks, but this
   assumption needs verification in the cohorts with robust increases in
   cardiovascular risk.

   Our study poses several limitations. The subjects investigated (both
   healthy and AD populations) were overweight, potentially contributing
   to increases in inflammatory markers, and profiles might be different
   in a lean population. Another limitation is that while AD and healthy
   subjects were matched for BMI and age, psoriasis patients had higher
   BMIs, although our data were corrected for BMI (and also for other
   cardiovascular risk factors such as asthma, arterial hypertension,
   hypercholesterolemia, and diabetes mellitus). Larger future studies
   should be performed that will also include lean patients and control
   populations.

   In sum, we have characterized a blood AD signature that is profoundly
   different from psoriasis. This profile helps to better understand
   cardiovascular risk in AD, and might also aid in identifying biomarkers
   to monitor therapeutic responses. Targeted therapeutic blockade of
   specific immune axes, e.g. Th17/IL-23 in psoriasis or Th2 in AD, is
   needed to assess the contribution of polar cytokine activation to
   overall systemic inflammation, and its effect on cardiovascular
   comorbidity and biomarkers. These studies should also assess whether
   biomarkers are modifiable risk factors responsive to treatment, as
   suggested by their decline with cyclosporine A treatment in severe
   chronic AD^[213]33.

Methods

Patients and samples

   A cohort of 59 patients with moderate-to-severe AD (31 male and 28
   female patients; mean age 40.5 years, range 18–72 years; mean SCORAD
   54.1, range 34.5–89; mean blood eosinophils 6.91%, SD 4.94; median
   total serum IgE 2,412kU/L, IQR 5,028) was included in this study
   (Table [214]1, Supplementary Table [215]S6). 20 patients (33.9%)
   reported to have mild asthma, of which 11 (18.6%) were on asthma
   treatment (inhaler); 9 patients (16.1%) reported seasonal allergies. 11
   AD patients suffered from one or more of the following cardiovascular
   risk factors: Arterial hypertension (n = 8), diabetes mellitus (n = 4),
   or hypercholesterolemia (n = 7). Serum samples (n = 59) with
   corresponding lesional (n = 58) and nonlesional (n = 53) skin punch
   biopsies were collected. All methods were carried out in accordance
   with relevant guidelines and regulations. All experimental protocols
   were approved by the local institutional review boards (IRB of The
   Rockefeller University and the Icahn School of Medicine at Mount Sinai,
   both New York, NY). All patients gave written informed consent prior to
   inclusion. Lesional biopsies were obtained from chronic lesions, and
   non-lesional biopsies were taken from uninvolved skin in proximity but
   at least 4 cm away from lesional samples. 22 serum samples from
   moderate-to-severe psoriasis patients (18 male and 4 female patients;
   mean age 46.8 years, range 20–67 years; mean PASI 27.6, range
   11.5–45.9) and 18 from healthy control subjects (12 male and 6 female
   subjects; mean age 41.3 years, 24–55 years) were obtained as
   comparators (Table [216]1, Supplementary Table [217]S6). Washout
   periods prior to study inclusion were 4 weeks for systemic therapy
   (cyclosporine, oral steroids, azathioprine, mycophenolate mofetil and
   all other systemic immunosuppressants) and 2 weeks for phototherapy and
   topical corticosteroids for both psoriasis and atopic dermatitis. None
   of the subjects had any defined history of cardiovascular disease.

OLINK multiplex assay

   Serum samples were collected, centrifuged, and stored at −80 °C until
   further processing. Aliquots were analyzed with an OLINK Proseek®
   multiplex assay^[218]40,[219]94, a proximity extension assay (PEA)
   technology with oligonucleotide-labeled antibody probe pairs that bind
   to their respective targets^[220]41. Upon binding of antibody pairs to
   their respective targets, DNA reporter molecules bound to the
   antibodies gave rise to new DNA amplicons with each ID-barcoding their
   respective antigens. The amplicons were subsequently quantified using a
   Fluidigm BioMark^TM HD real-time PCR platform^[221]40. Serum was
   analyzed using Inflammation I, cardiovascular disease/CVD II, and CVD
   III multiplex panels, which contain a broad array of established and
   exploratory markers^[222]40. OLINK data per subject are given in
   Supplementary Table [223]S6.

Skin RT-PCR

   Real time-PCR was performed on AD-related genes as described
   previously^[224]95,[225]96. Results were normalized to the housekeeping
   gene hARP and log2-transformed for analysis. Primers and probes used
   are listed in Supplementary Table [226]S7.

Statistical analysis

   Statistical analysis was carried out using R-language (R-project.org)
   and packages available through the Bioconductor Project
   ([227]www.bioconductor.org).

Quantification of Protein levels

   Quality control of OLINK chip data was carried out using their standard
   quality control pipeline (QC)^[228]40,[229]94. A minor number of
   samples in each panel were excluded after this quality control
   procedure. A small batch effect (explaining 8.4% of the variance) was
   observed corresponding to the processing times. This plate effect was
   estimated through a linear model using a set of intra-plate control
   samples that were repeated in each plate. We corroborate that the batch
   was successfully adjusted for the set of 7 intra-plate samples (showed
   in duplicates in Supplementary Table [230]S6) presented in the
   statistical analysis for this paper.

Analysis of Protein profiles

   Protein expression profiles were modeled using linear models for
   high-throughput data on R’s limma framework. The model included Disease
   as a factor and covariates Age, Gender and BMI. Comparison between
   groups were estimated using an empirical Bayesian method^[231]97
   available in the limma package; this uses the variance across all genes
   to estimate per gene variance. After model estimation using residual
   maximum likelihood algorithms, hypothesis testing was conducted for
   comparisons of interest using contrasts under the general framework for
   linear models in the limma package. P-values from the moderated t-test
   were adjusted for multiple hypotheses using the Benjamini–Hochberg
   procedure, which controls the FDR (false discovery rate). A sensitivity
   analysis including asthma and cardiovascular (CVD) risk outcomes as a
   covariate was also carried out. Covariates with missing values were
   imputed as the mean (age, BMI) and as “not present” for binary
   comorbidities. Sensitivity analysis showed no departure from the
   attained conclusions due to imputation. Comparison of protein profiles
   among groups was carried out using linear models for high-throughput
   data on R’s limma framework. Protein annotations, as well as
   Gene-Protein relationships were obtained by using UniPro IDs and R’s
   AnnotationDbi package.

RT-PCR data

   Ct values were derived by normalizing Ct values to an endogenous
   control gene (hARP). Values under the limit of detection (LOD) were
   substituted by the 20% of the minimum value above the LOD. Data was
   log2-tranformed prior to analysis.

   Correlation between skin mRNA and protein profiles was evaluated using
   Pearson and Spearman correlation coefficients on log2-transformed
   levels. Data is presented in scatterplots with estimated linear
   regression and 95% confidence interval.

Pathway Enrichment Analysis

   Gene set over-representation analysis was performed using XGR
   software^[232]98 based on functional category including KEGG^[233]99,
   BioCarta^[234]100, REACTOME^[235]101, PID^[236]51 and MSigDB^[237]102.
   The significance of the overlaps was assessed using FDR < 0.05.

Data availability

   All data generated or analyzed during this study are included in this
   published article (and its Supplementary Information Files).

Electronic supplementary material

   [238]Supplementary Material^ (2.2MB, docx)
   [239]Supplementary Table S1^ (156.5KB, xls)
   [240]Supplementary Table S2^ (156.5KB, xls)
   [241]Supplementary Table S3^ (94.5KB, xlsx)
   [242]Supplementary Table S4^ (46.5KB, xlsx)
   [243]Supplementary Table S5^ (29KB, xls)
   [244]Supplementary Table S6^ (600.5KB, xls)

Acknowledgements