Dynamics of Multisystem Inflammatory Syndrome in Children (MIS-C) associated to COVID-19: steady severity despite declining cases and new SARS-CoV-2 variants—a single-center cohort study

Abstract

Multisystem Inflammatory Syndrome in Children (MIS-C) is a serious condition associated with SARS-CoV-2 infection. The relationship between SARS-CoV-2 variants of concern (VOCs) and the occurrence and severity of MIS-C is unknown. We analyzed the dynamics of MIS-C in the Milan metropolitan area (Italy) during the COVID-19 pandemic, focusing on the epidemiologic trends and disease severity in relation to different VOCs in a single-center study. Fifty-seven MIS-C patients (mean 8.3 ± 3.8 years) admitted to the Pediatric Department of Buzzi Children’s Hospital in Milan, Italy, between November 2020 and July 2022, were retrospectively included in the study. The SARS-CoV-2 variant was retrospectively identified from serological fingerprinting (profiles of serum antibodies targeting different variants of SARS-CoV-2 obtained by a label-free microarray biosensor) or by the variant of prevalence. Two main periods of MIS-C case accumulation were observed. The peak of MIS-C cases rate in December 2020 reached 0.6 cases per day, which is nearly double the rate observed in February 2022, despite the larger number of infected subjects. Although the WT variant exhibited a broader range of severity score values, the score distributions for the different variants do not show statistically relevant differences. Conclusion: The results clearly show a decrease in the incidence of MIS-C in relation to infections, but also support the concept that severity of MIS-C remained essentially unchanged across different virus variants, including Omicron. The course of MIS-C, once initiated, is independent from the characteristics of the triggering variants, although later variants may be considered less likely to induce MIS-C. What is known:
• MIS-C is a rare systemic inflammatory disorder that arises as a post-infectious complication temporally related to SARS-CoV-2 infection.
• Fluctuations in MIS-C incidence were observed throughout the pandemic, with the latest variants associated with a lower incidence.
What is new:
• The SARS-CoV-2 variant of infection can be retrospectively confirmed by serum antibody fingerprinting using a label-free microarray biosensor.
• Despite the decreasing incidence, MIS-C severity has remained essentially unchanged across SARS-CoV-2 variants.

Full text

Vol.:(0123456789)
European Journal of Pediatrics (2025) 184:327
https://doi.org/10.1007/s00431-025-06153-1
RESEARCH
Dynamics ofMultisystem Inflammatory Syndrome inChildren (MIS‑C)
associated toCOVID‑19: steady severity despitedeclining cases
andnew SARS‑CoV‑2 variants—a single‑center cohort study
ThomasCarzaniga1 · ValeriaCalcaterra2,3 · LucaCasiraghi1 · TommasoInzani1 · StephanaCarelli4,5 ·
GabrieleDelCastillo6 · DaniloCereda6 · GianvincenzoZuccotti2,7 · MarcoBuscaglia1
Received: 1 February 2025 / Revised: 17 April 2025 / Accepted: 23 April 2025 / Published online: 7 May 2025
© The Author(s) 2025
Abstract
Multisystem Inflammatory Syndrome in Children (MIS-C) is a serious condition associated with SARS-CoV-2 infection.
The relationship between SARS-CoV-2 variants of concern (VOCs) and the occurrence and severity of MIS-C is unknown.
We analyzed the dynamics of MIS-C in the Milan metropolitan area (Italy) during the COVID-19 pandemic, focusing on the
epidemiologic trends and disease severity in relation to different VOCs in a single-center study. Fifty-seven MIS-C patients
(mean 8.3 ± 3.8years) admitted to the Pediatric Department of Buzzi Children’s Hospital in Milan, Italy, between November
2020 and July 2022, were retrospectively included in the study. The SARS-CoV-2 variant was retrospectively identified from
serological fingerprinting (profiles of serum antibodies targeting different variants of SARS-CoV-2 obtained by a label-free
microarray biosensor) or by the variant of prevalence. Two main periods of MIS-C case accumulation were observed. The
peak of MIS-C cases rate in December 2020 reached 0.6 cases per day, which is nearly double the rate observed in Febru-
ary 2022, despite the larger number of infected subjects. Although the WT variant exhibited a broader range of severity
score values, the score distributions for the different variants do not show statistically relevant differences. Conclusion: The
results clearly show a decrease in the incidence of MIS-C in relation to infections, but also support the concept that severity
of MIS-C remained essentially unchanged across different virus variants, including Omicron. The course of MIS-C, once
initiated, is independent from the characteristics of the triggering variants, although later variants may be considered less
likely to induce MIS-C.
What is known:
• MIS-C is a rare systemic inflammatory disorder that arises as a post-infectious complication temporally related to SARS-CoV-2 infection.
• Fluctuations in MIS-C incidence were observed throughout the pandemic, with the latest variants associated with a lower incidence.
What is new:
• The SARS-CoV-2 variant of infection can be retrospectively confirmed by serum antibody fingerprinting using a label-free microarray
biosensor.
• Despite the decreasing incidence, MIS-C severity has remained essentially unchanged across SARS-CoV-2 variants.
Keywords Multisystem inflammatory syndrome· Children· SARS-CoV-2· Infection· Variants of concern· Serum
antibody fingerprint· Label-free microarray biosensor
Introduction
Multisystem Inflammatory Syndrome in Children (MIS-C)
is a rare but serious condition associated with SARS-CoV-2
infection. Among children with confirmed exposure to
COVID-19, less than 1% developed MIS-C [1]. Epidemio-
logical characteristics include a higher prevalence in males,
a peak incidence between 6 and 12 years of age, and distinct
Communicated by Tobias Tenenbaum
Thomas Carzaniga and Valeria Calcaterra are joint first authors.
Gianvincenzo Zuccotti and Marco Buscaglia are joint last authors
and corresponding authors.
Extended author information available on the last page of the article
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European Journal of Pediatrics (2025) 184:327327 Page 2 of 12
racial or ethnic predispositions, with Hispanic and non-His-
panic Black individuals being at the highest risk [2–4].
The pathogenic mechanism of MIS-C remains uncertain,
though the most widely accepted theory involves a cytokine
storm and the role of adaptive immunity following SARS-
CoV-2 infection. Additionally, certain intrinsic susceptibility
factors in the host have been identified, and molecular mim-
icry has also been suggested as part of the disease’s patho-
genesis [5, 6]. MIS-C typically occurs a few weeks after a
child has been infected with the virus, involving inflamma-
tion across multiple organ systems. The most common mani-
festations involve the gastrointestinal tract, cardiovascular
system, hematological system, and mucocutaneous system.
The respiratory tract, neurological system, musculoskel-
etal system, and kidneys are less frequently affected. Most
patients are previously healthy children without significant
comorbidities, aside from asthma and obesity [7].
Since late 2020, several SARS-CoV-2 variants of con-
cern (VOCs), including Alpha, Beta, Gamma, Delta, and
Omicron, have emerged, significantly influencing the spread
and severity of COVID-19. Each VOC carries mutations
that can affect the virus's transmissibility, severity of ill-
ness, and ability to evade immune responses from either
natural infection or vaccination [8, 9]. With the emergence
of SARS-CoV-2 VOCs, interest has grown in understanding
the relationship between these variants and the occurrence of
MIS-C. Some studies have highlighted differences in MIS-C
incidence among patients infected by various SARS-CoV-2
variants. Notably, MIS-C incidence was lower during the
Omicron wave compared to the Delta wave, with Omicron-
related cases perceived as generally milder than those seen
in the periods of the previous variants [10–12]. However, it
remains unclear whether VOCs directly influence MIS-C
occurrence or if the increase in MIS-C cases is merely a
reflection of the overall rise in infections driven by these var-
iants. Moreover, fluctuations in MIS-C incidence throughout
the pandemic may also be attributed to a combination of
various factors, including rising seroprevalence due to previ-
ous infections and/or vaccination [13].
The aim of this study was to analyze the dynamics of
MIS-C in the metropolitan area of the city of Milan (Italy)
during the COVID-19 pandemic, focusing on the epide-
miologic trends and disease severity in relation to different
VOCs, both before and after the availability of vaccinations
for children.
Methods
Subjects withMIS‑C
This retrospective study included 57 children admitted to the
Pediatric Department of Buzzi Children’s Hospital in Milan,
Lombardy region, Italy, between November 2020 and July
2022 who were diagnosed with MIS-C according to CDC
criteria [14]. The hospital is a regional center of reference
for the diagnosis and treatment of MIS-C during pandemic.
To assess multisystem involvement severity, specific scores
(0–2 points) were assigned to multiple clinical domains:
renal, cardiac, gastrointestinal, neurological, pulmonary,
dermatological/mucosal, endocrine, metabolic, and electro-
lyte disturbances. Summing these domain scores provides
an overall severity measure. Additionally, key clinical out-
comes, including ICU admission requirement, hospitaliza-
tion duration, and fever occurrence, were also considered.
Criteria details are in Supplementary Note S1 [15]. Demo-
graphic and clinical data, as well as vaccination status, were
recorded for all patients. The study adhered to the Declara-
tion of Helsinki and received approval from the institutional
ethics committee (MI-1, n. 0034170; protocol number 2021/
ST/138). Written consent was obtained from the patients’
guardians after explaining the study’s objectives.
Auxological parameters
Physical assessments of children included evaluations of
weight, height, BMI (Table1). Height was measured with
the patient standing barefoot using a Harpenden stadiometer,
providing an accuracy of approximately ± 1 mm. Weight was
recorded with the patient in underwear, standing upright on
a platform scale, with a precision of about ± 100 g [16, 17].
BMI was calculated by dividing body weight (in kilograms)
by the square of height (in meters), and BMI z-scores were
derived using WHO reference standards [18]. The SARS-
CoV-2 variant that caused the infection indicated in Table1
was retrospectively identified from serological fingerprinting
or by the variant of prevalence, as described in the “Results”
section.
Samples andreagents
Plasma samples were obtained from patients at the Pedi-
atric Department, Buzzi Children’s Hospital, Milan. All
receptor-binding domains (RBD) of SARS-CoV-2 spike
proteins (WT-RBD, α-RBD, γ-RBD, δ-RBD, o-RBD,
o1-RBD, o2-RBD, and o4/5-RBD) obtained from HEK293
human embryonic kidney immortalized cell line were pur-
chased from Sino Biological (Beijing, China). Nucleocap-
sid protein and Rabbit polyclonal antibody anti-human IgG
were obtained from Abcam (Cambridge, UK; product code
ab273530 and ab7155). Trimeric spike protein HexaPro was
donated by Anton Schmitz and Günter Mayer [19, 20]. All
the buffers and reagents were purchased from Merck (Darm-
stadt, Germany) and prepared using Milli-Q pure water.
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European Journal of Pediatrics (2025) 184:327 Page 3 of 12 327
Serum antibody fingerprints
The profiles of serum antibody targeting different variants
of SARS-CoV-2 were obtained by label-free microarray
biosensor as described in [21]. Briefly, antigen proteins
and control antibodies were covalently immobilized onto
the surface of Reflective Phantom Interface (RPI) glass
sensing chips in different spots forming a microarray [22].
After ozone cleaning, the chips were dip-coated with a
copolymer of dimethylacrylamide (MCP2, Lucidant Poly-
mers Inc., Sunnyvale, CA, USA) [20], and the arrays of
spots were produced by an automated, non-contact dis-
pensing system (sciFLEXARRAYER S3; Scienion AG,
Berlin, Germany). Sample spikes were performed by add-
ing 13 μL of patients’ plasma with a micropipette into
disposable cartridges hosting the sensing chip, previously
filled with 1.3 mL of measuring buffer (PBS 1 ×, pH 7.4,
SDS 0.02% and sodium azide 0.02%). The cartridges were
kept at 25 °C during the measurement through a thermal-
ized holder, and rapid mixing of the solution was provided
by magnetic stirring. A custom optical apparatus acquired
time sequences of images of the spotted surface of the
sensing chips. The amount of surface immobilized anti-
gens was obtained from the brightness of each spot before
the injection of the sample. Similarly, the amount of anti-
bodies binding the surface antigens was obtained from the
increase with time of the brightness of each spot after the
sample injection. Each data point is obtained by averaging
the signal from at least five spots with identical composi-
tion. For each antigen, the growth unit GU was obtained
from the initial slope of the binding curves divided by the
amount of surface antigens, and the relative GU (RGU)
was computed as the ratio between the GU of each variant
and that of WT variant.
Statistical methods
Statistical analysis was made by using GraphPad Prism
version 10. The possible influence of children’s age on
the severity score was assessed by analyzing the depend-
ence between these two parameters using linear regression
(Supplementary Note S2). The possible influence of sex
was assessed using Student’s t-test on the score distribu-
tions for males and females (Supplementary Note S3). The
latency between MIS-C cases and COVID-19 infections was
obtained from the analysis of the linear regression between
the two sets of data with variable time delay (Supplementary
Note S4). The uncertainties of the cumulative numbers n of
MIS-C cases, infections, and vaccinations were obtained as
1.96
√n
, representing 95% confidence interval for a Pois-
son distribution of events. The distributions of severity
score values for the different SARS-CoV-2 variants were
compared by using one-way analysis of variance (ANOVA)
with Tukey’s post hoc analysis (Supplementary Note S5).
All tests with P values > 0.3 were notconsidered statisti-
cally significant.
Results
The number ofMIS‑C cases showed two main peaks
followingcorresponding peaks ofinfections
In the two-year period covered by this study, from November
2020 up to July 2022, 57 children with age up to 17-year-old
were diagnosed with MIS-C after admission at the Pediatric
Department of Buzzi Children’s Hospital in Milan, Italy.
No pre-existing illnesses or known comorbid conditions
were found among the patients. A summary of the main
Table 1 Patient characteristics
at admission Characteristics Total Variant
WT Alpha Delta Omicron
Unvaccinated Vaccinated
No. of patients 57 27 6 12 8 4
Males (%)
Female (%)
75
25
82
18
100
0
58
42
75
25
50
50
Age (years) avg
min/max 8.3 ± 3.8
2/16
8.2 ± 3.9
2/15
8.0 ± 4.7
2/14
7.5 ± 3.8
3/16
9.0 ± 3.5
5/16
10.8 ± 2.1
8/13
BMI (kg/m2) avg
min/max 18.1 ± 3.6
12.8/28.4
18.5 ± 3.9
13.5/28.4
17.1 ± 2.8
15.1/22.3
17.7 ± 3.7
12.8/23.9
18.6 ± 3.5
14.4/23.7
17.2 ± 3.0
13.2/20.1
BMI z-score avg
min/max 0.14 ± 1.2
− 2.6/2.6
0.02 ± 1.1
− 2.2/1.9
− 0.17 ± 0.8
− 1.1/0.7
0.35 ± 1.5
− 2.4/2.3
0.68 ± 1.0
− 0.8/2.6
− 0.26 ± 1.9
− 2.6/1.5
Severity score avg
min/max 12.2 ± 3.3
3/21
12.5 ± 3.9
3/21
11.0 ± 1.8
9/13
11 ± 3.2
6/17
12.9 ± 2.9
9/17
13.5 ± 1.3
12/15
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European Journal of Pediatrics (2025) 184:327327 Page 4 of 12
characteristics at admission for the cohort of subjects con-
sidered in this study is presented in Table1. We analyzed
the temporal distribution of the MIS-C cases in relation to
the characteristics of the subjects, of the infection and of
the severity of the disease. Figure1a reports the age and
the date of hospital admission for each subject, as well as
the sex (color of the points) and the severity of MIS-C (size
of the points) quantified by the score proposed in [15]. The
age distribution of the subjects (Fig.1b) is rather uniform
between 3 and 12 years, whereas fewer cases have been
reported outside this range of age. Among the subjects with
MIS-C, the number of males (43) is considerably larger, in
agreement with previous studies [2–4]. Despite the effect of
age and sex on the number of cases, the severity score does
not display significant dependences on these factors (see
Methods and Supplementary Notes S2 and S3). Figure1a
shows two main periods with accumulation of MIS-C cases:
in December 2020 (P1) and February 2022 (P2). Figure1c
(orange line) reports the temporal dependence of the number
of MIS-C cases per day computed by 30-day moving aver-
age. As expected from the data of Fig.1a, two peaks emerge
in the period P1 and P2. Figure1c also reports the infection
rate with SARS-CoV-2 virus over the Italian population
(black line) [23] and the population of Lombardy (grey line)
[24]. Beside a scaling factor due to the different size of the
basins, the time behavior of the infections is quite similar
for the two populations. The peaks of MIS-C follow by 25
(P1) or 21 (P2) days the corresponding peaks of infection
(see Methods and Supplementary Note S4). The maximum
rate of MIS-C cases for peak P1 corresponds to 0.6 cases per
day diagnosed at the Buzzi Children’s Hospital. This value is
almost double than that of P2. In contrast, the peak of infec-
tion in proximity of P1 is about 5 times smaller than that of
P2, hence suggesting a different dynamics of the incidence
of infection and MIS-C cases.
The incidence ofMIS‑C tends todecrease withtime
In order to investigate the possible causes of the observed
decrease of the number of MIS-C cases in the period P2, we
considered the effect of the vaccination campaign, which
in Italy started on June 3, 2021, for the pediatric popula-
tion aged 12–15 years, and on December 16, 2021, for the
children aged 5–11 years. Accordingly, we considered two
Fig. 1 Dynamics of MIS-C cases and SARS-CoV-2 infection. a
Time distribution of MIS-C cases diagnosed at Pediatric Depart-
ment of Buzzi Children’s Hospital in Milan, Italy. For each data
point, the horizontal axis indicates the initial time of hospitalization,
the vertical axis reports the age of the child, the color of the circle
represents the sex(cyan for males and magenta for females), and the
circle size indicates the severity score [15] assigned to each case. b
Age distribution of MIS-C cases of panel a. Each abscissa tick repre-
sents 2 cases. c Time dependence of MIS-C cases reported in panel a
(orange), and of SARS-CoV-2 infections in Italy (black) [23] and in
Lombardy region (grey) [24], computed as 30-day moving averages
of daily data. The data of MIS-C cases are multiplied by a factor 2.5
× 105 and the data of the number of infection in Lombardy are mul-
tiplied by a factor 4 to facilitate the comparison. The two peaks of
MIS-C cases of December 2020 and February 2022 are indicated as
P1 and P2, respectively. The shadow curves represent the percentage
of vaccinated children in Lombardy with ages 0–11 (dark violet) and
12–19 (light violet)
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European Journal of Pediatrics (2025) 184:327 Page 5 of 12 327
age groups, 0–11 and 12–19 years. The fraction of subjects
vaccinated with the first dose for these age groups among
Lombardy population is shown in Fig.1c as dark and light
violet shadows, respectively. At the time of MIS-C peak P2,
the fraction of vaccinated subjects of the 12–19 years group
was above 80% and constant, whereas the growth of MIS-C
cases at P2 is almost coincident with the increase of the
fraction of vaccinated subject of the 0–11-year group, which
however reaches a stable value below 30%. Given the differ-
ent vaccination status of the two age groups, we investigated
possible differences in the distribution of MIS-C cases. For
direct comparison with the data of the number of infections,
we considered two age groups of 0–9 and 10–19 years, and
two periods, from November 2020 up to December 15, 2021
(T1), hence before P1 and before the vaccination of the age
group 0–11 years, and from December 16, 2021, up to July
2022 (T2). Figure2 shows that the incidence of MIS-C (blue
bar) relative to the incidence of the infection (red bar) is
larger in the period T1 (panels a and b) then in T2 (panels c
and d), consistently with peaks P1 and P2 in Fig.1. Overall,
the MIS-C incidence is larger for the age group 0–9 (panels a
and c), despite the fact that this group shows a slightly lower
incidence of infection then the age group 10–19 (panels b
and d). For what concerns the vaccinated pediatric popula-
tion in Lombardy in period T2 (green bar in Fig.2c, d),
the fraction of vaccinated subjects in the age group 10–19
is more than 3 times larger than that of the age group 0–9.
Despite this, the MIS-C incidence is only 20% smaller for
this age group. Considering that the MIS-C incidence for
the age group 10–19 was already smaller before the vaccina-
tion campaign (panels a and b), the data under consideration
do not allow to correlate the observed decrease of MIS-C
cases in period T2 to the number of vaccinated subjects in
the pediatric population. Therefore, we also investigated the
possible effect of SARS-CoV-2 variants on MIS-C cases.
The SARS‑CoV‑2 variant ofinfection thatcaused
MIS‑C can be retrospectively identified byserum
antibody fingerprinting
The variant of a SARS-CoV-2 infection can be retrospec-
tively identified from the repertoire of serum antibody
against the different variants of RBD domain of spike
protein, as shown in [21, 26]. Accordingly, we analyzed
the blood samples that were collected at the time of the
hospital admission and then stored for a subset of the
Fig. 2 MIS-C patients in rela-
tion to the pediatric population.
The fraction of MIS-C cases
(blue) diagnosed at Pediatric
Department of Buzzi Children’s
Hospital in Milan, Italy, relative
to the population of Lombardy
region [25], the fraction of
subjects infected by SAR-
CoV-2 (red) among the Italian
population [24], and the fraction
of vaccinated subjects (green)
in Lombardy region [25] are
reported for the age groups 0–9
(a, c) and 10–19 (b, d), and for
the periods November 9, 2020–
December 15, 2021 (a, b), and
December 16, 2021–July 2,
2022 (c, d). The reported values
represent the number of subjects
divided by the corresponding
cohort, i.e. the pediatric popula-
tion of age 0–9 (a, c) and 10–19
(b, d) at February 2023 in Italy
(red bars) or in Lombardy (blue
and green bars). Error bars rep-
resent 95% confidence interval
(see the “Methods” section).
For data of infected and vacci-
nated subjects, the error bars are
smaller than 1% (not displayed).
The values of MIS-C cases are
multiplied by 10−6 as indicated
in the axis scale for direct com-
parison with the other data
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European Journal of Pediatrics (2025) 184:327327 Page 6 of 12
children who developed MIS-C. Figure3 reports a selec-
tion of Ig fingerprints in which each box is organized into
three parts: the meter on the left-hand side reports the
amount of Ig targeting the full spike protein (grey line) and
the WT-RBD (orange line) expressed as growth unit (GU)
of the biosensor signal; the meter on the right-hand side
reports the GU values corresponding to the amount of Ig
targeting the nucleocapsid protein; the radar chart in the
center reports the RGU profiles computed as the values of
GU for different variants (orange line) divided by the GU
for WT-RBD taken as reference (black line). As shown
in the fingerprints of Fig.3, the RGU profiles are uncor-
related with the overall amount of Ig against spike protein
(left-hand meter) or nucleocapsid (right-hand meter) but
provide an independent criterion for the retrospective iden-
tification of the variant of infection. In the radar chart, an
RGU for a specific variant (orange vertex) larger than the
black line reference indicates an Ig amount larger than
that of WT-RBD, and this feature is associated to a past
infection with that variant of SARS-CoV-2 [21]. Table2
reports a summary of the variants of infection assigned by
serum antibody fingerprinting and of the characteristics
of the patients.
The severity ofMIS‑C isnotrelated
totheSARS‑CoV‑2 variant ofinfection
Figure4 reports the incidence (grey shadow) for each of
the four main variants of the period under study (i.e. WT,
Alpha, Delta, and Omicron). Each MIS-C case was ascribed
to one of these variants of infection either by serum anti-
body fingerprinting (orange diamonds) or considering the
variant of prevalence (i.e., incidence larger than 50%) at
the time of sample collection (yellow circles). Among the
31 samples tested by serum antibody fingerprinting, only
5 were assigned to the variant preceding the variant that
was emerging on national scale (Alpha instead of Delta in
panel b and Delta instead of Omicron in panel c), and one
was assigned to the emerging variant (Alpha instead of WT
in panel b). Moreover, only four samples corresponded to
subjects vaccinated with the WT variant before MIS-C. The
Fig. 3 Serum antibody fingerprints of MIS-C subjects against anti-
gens of different SARS-CoV-2 variants. a Cartoon of the assay
design: Ig antibodies bind the surface-immobilized antigens (i.e.,
RBD of spike protein or full spike protein). b Legend of the finger-
print diagram. The left-side meter reports the quantification of Ig
in terms of GU of two WT antigen spots, as indicated. The right-
side meter reports the quantification of anti-nucleocapsid antibod-
ies expressed as GU. The radar chart reports the values of RGU for
Alpha, Gamma, Delta, and four different Omicron RBD variants. The
thick black contour line represents the amount of antibodies binding
to WT-RBD taken as reference, hence corresponding to RGU = 1. c
Selection of Ig fingerprints obtained for sixteen samples of subjects
affected by MIS-C and infected with different variants of SARS-
CoV-2, as indicated: WT (first row), Alpha (second row), Delta (third
row) and Omicron (fourth row)
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European Journal of Pediatrics (2025) 184:327 Page 7 of 12 327
serum antibody fingerprint of these samples was similar to
that of WT infection, as expected [21], and the infection
causing the MIS-C was ascribed to Omicron variant accord-
ing to the prevalence (purple squares in Fig.4d).
The retrospective identification of the variant of infec-
tion enables to investigate the dependence of the severity of
MIS-C on SARS-CoV-2 variants. Figure5 reports the sever-
ity score assigned to each MIS-C case for the different vari-
ants of infection. Despite a larger distribution of score values
of the WT variant, which is ascribed to the larger number
of cases, both the median values and confidence intervals of
the score (as well as the average and the standard deviation)
are similar for all the variants. As also confirmed by analysis
of variance (see the “Methods” section and Supplementary
Note S5), the distributions of severity score values reported
in Fig.5 do not show significant dependences on the variant
of infections.
Discussion
The World Health Organization (WHO) declared COVID-19
a pandemic in March 2020 [28]. In the spring of 2020, a sig-
nificant increase in Kawasaki disease-like syndrome among
children was reported, leading to a multisystem inflamma-
tory condition known as MIS-C [29–32]. MIS-C has been
shown to be a potentially life-threatening illness, character-
ized by severe and aberrant systemic inflammation leading
to nonspecific symptoms, such as gastrointestinal, cardiac,
respiratory, hematological, and neurological disorders [15].
The incidence rates of MIS-C vary across different geo-
graphic regions. Increased case numbers have been observed
in Europe, the Americas, Africa, South Asia, and the Middle
East, while East Asian countries report significantly fewer
cases. This geographic distribution of MIS-C incidence is
likely influenced by a combination of environmental fac-
tors, social determinants of health disparities, and genetic
backgrounds [32, 33].
A wide clinical spectrum of MIS-C presentations has
been described. In our study, we proposed a severity score
designed to assign a graded evaluation for each affected
organ, based on both clinical and biochemical parameters.
Other classifications have been reported in the literature aim-
ing to stratify MIS-C severity [34, 35]. Notably, Rao etal.
[36] applied heterogeneity-adaptive latent class analysis and
identified three distinct clinical profiles: Class 1, compris-
ing nearly half of the patients, was associated with the most
severe manifestations, including frequent ICU admissions,
elevated inflammatory markers, and significant involve-
ment of the cardiac, respiratory, and renal systems. Class
2 represented a moderate presentation, with multi-organ
involvement and features overlapping with acute COVID-
19, while Class 3 reflected milder cases. Similarly, Ma etal.
[35] identified three clusters, distinguished by predominant
respiratory symptoms (8.0%), cardiac complications and
shock (37.6%), and clinically mild, undifferentiated cases
(54.5%). All these approaches aim to stratify MIS-C patients
based on clinical presentation and severity. However, while
our proposed score quantitatively grades severity using more
specific clinical and laboratory parameters, the other clas-
sifications group patients into phenotypic subtypes without
assigning a numeric score. These complementary methods
enhance our understanding of the clinical heterogeneity of
MIS-C and may support more tailored patient management
and treatment strategies.
A global decline in MIS-C cases has been observed
alongside the evolution of the SARS-CoV-2 virus [37]. US
national surveillance data showed that the proportion of total
MIS-C cases decreased over time with sequential SARS-
CoV-2 variants, with 58.0% occurring pre-Delta and 16.1%
occurring during the Omicron predominant phase [35].
U.K. have likewise demonstrated a significant decline in
MIS-C incidence during the Omicron-predominant period,
both in terms of absolute case numbers and clinical sever-
ity [38]. Similarly, research from Asian countries such as
South Korea [12] and Japan [39] showed a declining trend
Table 2 Patients whose variant
of infection was assigned by
serum antibody fingerprinting
*Vaccinated subjects provided serum antibody fingerprints coherent with WT exposure, in agreement with
vaccination by WT variant. For these subjects, the variant of infection was assumed to be the variant of
prevalence (Omicron)
Characteristics Variant Vaccinated*
WT Alpha Delta Omicron
Fraction of
tested patients 9/27 4/6 9/12 5/8 4/4
Males (%)
Female (%)
78
22
100
0
45
55
80
20
50
50
Age (years) avg
min/max 6.9 ± 3.0
2/10
8.5 ± 4.4
4/14
7.7 ± 4.0
4/16
10.2 ± 3.9
5/16
10.8 ± 2.1
8/13
Severity score avg
min/max 14.1 ± 3.6
9/21
11.0 ± 1.6
9/13
11 ± 3.3
6/17
12.4 ± 3.0
9/17
13.5 ± 1.3
12/15
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European Journal of Pediatrics (2025) 184:327327 Page 8 of 12
in MIS-C cases during the Omicron wave. Accordingly,
MIS-C is generally perceived as a less threatening disease
in the latest stage of the pandemic, but this can be ascribed
to the gained experience and preparedness of medical cent-
ers enabling more rapid and effective treatments. Specifi-
cally, it was reported that Omicron-related MIS-C cases
have milder symptoms and organ damage than previous
variants, even though the variant itself is more transmissible
[40]. As reported, the relative proportion of children in the
respiratory as well as shock and cardiac clusters gradually
decreased after the emergence of the Omicron variant in the
USA, with the more mild, undifferentiated cluster predomi-
nating [35]. Studies in the UK reported a marked reduction
in hospitalizations and ICU admissions for MIS-C post-
Omicron emergence, with milder phenotypes predominating
[41]. In Asian countries a shift toward less severe clinical
presentations was also reported [12,39].
In order to unveil hidden correlations of MIS-C dynam-
ics with the pandemic evolution, we analyzed in detail the
MIS-C cases treated at the main pediatric hospital of the
city of Milan (Italy), a reference center for pediatric patients
with complex conditions in the whole Lombardy region, an
area with a population of nearly 10 million inhabitants. The
results confirm a decrease in the incidence of MIS-C in
relation to infections. Conversely, our data also support the
notion that the severity of MIS-C in hospitalized patients
remains largely consistent across different viral variants,
including Omicron.
As shown in Fig.1, the number of MIS-C cases tends to
follow the infections up to a certain extent. The fact that the
Fig. 4 SARS-CoV-2 variants of infection of MIS-C cases. Percentage
of infections by WT (a), Alpha (b), Delta (c), or Omicron (d) variants
in Italy (grey shadow) [27]. Each panel also reports the MIS-C sam-
ples whose infection was ascribed to the corresponding variant. The
data points and the vertical lines indicate the time of hospital admis-
sion of the subjects affected by MIS-C. The retrospective assignment
of the variant of infection of MIS-C subjects was based on serum
antibody fingerprinting (orange diamonds) or on variant prevalence
(more than 50% incidence) at the time of hospital admission (yellow
circles). Purple squares in panel d represent patients known to be vac-
cinated, which resulted as WT profile by serum antibody fingerprint
Fig. 5 Comparison of the severity score of MIS-C with the SARS-
CoV-2 variant of infection. The score assigned to each MIS-C case
according to the progression of the disease is reported for the differ-
ent variants responsible for the infection. For each case, the variant
displayed in the abscissa was identified by serum antibody fingerprint
(orange diamonds) or as the variant of prevalence at the date of hos-
pitalization (yellow circles). Purple squares represent MIS-C subjects
vaccinated before the infection. For each variant, the median and the
95% confidence interval are indicated by horizontal bars
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European Journal of Pediatrics (2025) 184:327 Page 9 of 12 327
MIS-C peak P2 in Fig.1 is smaller than P1, despite a larger
peak of infections, can be ascribed either to the decrease of
the susceptible population, due to vaccination and previous
infections, or to the characteristics of the virus variants.
In the early phase of the pandemic, the incidence of
MIS-C among unvaccinated subjects was estimated as 300
per million SARS-CoV-2 infections in persons younger than
21 years [42]. A decrease in the incidence of MIS-C during
the Delta and the Omicron waves and a protective effect due
to vaccination has been shown [43], down to 0.6 cases per
million among vaccinated children [44]. In our population,
comparing different pediatric age groups (Fig.2), which had
a different vaccination history, we could not derive a direct
protective effect of vaccination against MIS-C. Moreover,
among the 12 MIS-C cases ascribed to Omicron infection,
as much as 4 were vaccinated, although with the first vaccine
version against WT strain. However, the overall reduction of
infections due to the vaccination campaign clearly yielded
also a reduction of MIS-C cases down to rare events.
As highlighted by Castaldo etal. [33], various factors could
potentially explain the observed changes in the incidence and
severity of MIS-C. These include alterations in the human
immune response, the gradual development of immunological
memory to SARS-CoV-2 over time, mutations in critical amino
acids of the S protein in VOCs, and the interplay between the
immune response triggered by vaccination and the reduced neu-
tralization efficacy of vaccines against VOCs.
In contrast, the factors that could affect the severity of
MIS-C are unknown [45, 46]. According to the data col-
lected in this study, the severity of MIS-C does not show
clear trends with age or sex of the children, or virus vari-
ant, despite the decrease of the number of cases. In particu-
lar, we assigned each MIS-C case to a variant of infection
according to serum antibody fingerprinting and data on the
variant on prevalence in the period of the hospitalization
of the children. The analysis of serum antibody repertoire
confirmed the infections due to largely prevalent variants,
but also enabled a correct assignment in case of coexistence
of different variants (Fig.4). As expected, the MIS-C peak
P1 in Fig.1c is all due to the WT variant, whereas the peak
P2 can be predominantly ascribed to the Omicron variant.
Given the smaller incidence of MIS-C in P2 relative to the
infected population (Figs.1 and 2), we cannot rule out a
smaller tendency of the Omicron variant to induce MIS-C.
In contrast, the severity of MIS-C does not show a decreas-
ing trend with time and hence with the variant progression
(Fig.5). Therefore, if the overall decrease of the number
of MIS-C cases cannot be decoupled from the progression
of the SARS-CoV-2 variants, because of the concomitant
decrease of the susceptible population, the severity of
MIS-C seems unaffected and rather stable across the differ-
ent variants of infection. Thus, once initiated, the course of
MIS-C appears to be independent of the characteristics of
the triggering variants, although later variants may be less
likely to induce MIS-C.
When conducting this research, it is important to consider
potential limitations. Firstly, the small sample size restricts
the robustness of the analysis; thus, studies on larger cohorts
are needed to expand the sample size and validate these find-
ings. Secondly, MIS-C cases are from a single center, which
may limit the generalizability of our results, as variations
in patient demographics, clinical practices, and institutional
protocols across different centers could influence outcomes.
Furthermore, there is considerable variability among the
participating populations, including factors such as age and
vaccination status. Additionally, another limitation of our
study is related to the severity scale employed. Currently,
there are no globally validated severity scales for MIS-C;
therefore, caution should be exercised in interpreting our
findings, as the scale we used has been validated only within
a specific pediatric population. Future studies utilizing clus-
ter analysis could help refine surveillance case definitions
and more effectively identify patient groups at higher risk
for severe outcomes. Finally, the identification of the SARS-
CoV-2 variant was conducted retrospectively.
In conclusion, our results clearly indicate a decline in the
incidence of MIS-C associated with COVID-19 infections.
The potential role of a combination of factors in the reduc-
tion of MIS-C incidence over time cannot be excluded, even
though precisely quantifying the contribution of each factor
is challenging. However, our results also reinforce the idea
that the severity of MIS-C in hospitalized pediatric patients
has remained largely consistent across various virus vari-
ants. Once MIS-C is initiated, its progression appears to be
independent of the specific characteristics of the triggering
variants.
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s00431- 025- 06153-1.
Acknowledgements The authors thank Anton Schmitz and Günter
Mayer for the generous gift of the HexaPro spike protein and Drs Erika
Maghraby and Letizia Messa for their collaboration in sera preparation.
Author contributions T.C. and L.C. run analysis and prepared figures,
V.C.and G.V followed the patients and provided samples, S.C. pro-
cessed samples, T.I., G.D. and D.C. provided and analysed data, V.C.
and M.B. wrote the main manuscript text, all authors reviewed the
manuscript.
Funding Open access funding provided by Università degli Studi
di Milano within the CRUI-CARE Agreement. The project received
contributions from (1) Bando Cariplo Networking research and train-
ing post-COVID-19 protocol number 2021–4490; (2) HORIZON-
HLTH-2021-CORONA-01 CoVICIS, project number 101046041; (3)
PRIN2022: prot. 20228pnnjl e prot. 2022H7MH23 (“MuSIx” pro-
ject); and (4) Ministero dell’Università e della Ricerca, CN00000041
“National Center for Gene Therapy and Drugs based on RNA
Technology,” Spoke 5 and Spoke 9 (CUP G43 C22001320007,
PNRR MUR-M4 C2-Investimento 1.4, funded by European Union
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

European Journal of Pediatrics (2025) 184:327327 Page 10 of 12
- NextGenerationEU). Luca Casiraghi and Tommaso Inzani were sup-
ported by young scientist fellowships from the UNIMI GSA-IDEA
project.
Data availability Data and data sources are provided within the
manuscript.
Declarations
Ethics approval The Ethics Committee Milano Area 1 (protocol num-
ber 0037072) granted approval for the study.
The study was conducted according to the guidelines of the Declaration
of Helsinki, and approved by the Institutional Review Board Territo-
rial Area 1_Milano (Protocol number n. MI-1, n. 0034170; protocol
number 2021/ST/138).
Consent to participate Informed consent was obtained from all parents
and/or guardians involved in the study.
Competing interests The authors declare no competing interests.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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European Journal of Pediatrics (2025) 184:327327 Page 12 of 12
Authors and Aliations
ThomasCarzaniga1 · ValeriaCalcaterra2,3 · LucaCasiraghi1 · TommasoInzani1 · StephanaCarelli4,5 ·
GabrieleDelCastillo6 · DaniloCereda6 · GianvincenzoZuccotti2,7 · MarcoBuscaglia1
* Gianvincenzo Zuccotti
gianvincenzo.zuccotti@unim.it
* Marco Buscaglia
marco.buscaglia@unimi.it
Thomas Carzaniga
thomas.carzaniga@unimi.it
Valeria Calcaterra
valeria.calcaterra@unipv.it
Luca Casiraghi
luca.casiraghi@unimi.it
Tommaso Inzani
tommaso.inzani@unimi.it
Stephana Carelli
stephana.carelli@guest.unimi.it
Gabriele Del Castillo
gabriele_del_castillo@regione.lombardia.it
Danilo Cereda
Danilo_Cereda@regione.lombardia.it
1 Department ofMedical Biotechnology andTranslational
Medicine, University ofMilan, Segrate20054, Italy
2 Department ofPediatrics, Buzzi Children’s Hospital,
Milano20154, Italy
3 Pediatrics andAdolescentology Unit, Department ofInternal
Medicine, University ofPavia, Pavia27100, Italy
4 Pediatric Clinical Research Center “Romeo ed Enrica
Invernizzi,” Department ofBiomedical andClinical Science,
University ofMilan, Milan, Italy
5 Center ofFunctional Genomics andRare Diseases, Buzzi
Children’s Hospital, Milan20154, Italy
6 Prevention Operational Unit, General Directorate ofWelfare,
Lombardy Region, Milan, Italy
7 Department ofBiomedical andClinical Sciences, L. Sacco,
University ofMilan, Milan20157, Italy
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