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Multisystem inflammatory syndrome in children (MIS-C) is a relatively rare, but potentially life-threatening, post-infectious phenomenon, with an incidence of 2 per 100,000 SARS-CoV-2

persons younger than 21 years previously infected with SARS-CoV-2.1 A significant proportion of children with MIS-C undergo rapid clinical deterioration, characterised by shock, systemic

inflammation, and cardiac dysfunction.2,3,4 Despite the substantial morbidity, the majority of children make a full recovery and mortality rates are relatively low (1.7%).2 While most

children who develop MIS-C are previously healthy, studies have shown that around 25% of these children have pre-existing co-morbidities, with obesity and asthma most commonly

reported.5,6,7,8 Identifying predisposing and precipitating factors for MIS-C is crucial for identifying and protecting children at risk of developing MIS-C and its severe complications and

may also be of relevance for future pandemics.

Auger et al. sought to address risk factors for MIS-C, Kawasaki disease (KD) and complications of COVID-19 infections by conducting a longitudinal cohort study using hospital discharge

summary data from 1.2 million children and mothers in Quebec, Canada prior to and during the first year of the COVID-19 pandemic.9 This study is an exemplar of the potential utility of total

population data to investigate risk factors, identify co-morbidities and gain a deeper understanding of the impact of relatively rare but important conditions, and also highlights some of

the limitations of analyses of linked data. The results on the epidemiological and clinical features of MIS-C are in keeping with the current literature, with increased frequency in older

children (5–12 years) and males, and a high proportion of cardiac complications, gastrointestinal involvement, and shock.2 Moreover, when compared to KD and paediatric COVID-19, children

with MIS-C had a more severe disease phenotype with increased requirement for intensive care and longer hospital stay.3,7,10

This exploratory study identified several pre-pandemic conditions requiring hospitalisation that were associated with increased risk of MIS-C, including metabolic conditions (diabetes,

obesity, hypertension, and errors of metabolism), atopy, and cancer. Obesity was one of the earliest identified features associated with MIS-C5,6,7,8,11 and together with related metabolic

conditions (such as type 2 diabetes), has also been widely recognised as a risk factor for COVID-19 severity.12,13 Pre-existing altered metabolic environment leads to endothelial

dysfunction,14 increased systemic inflammation15 and impaired immune responses,16,17 which all may contribute to a dysregulated immune response to SARS-CoV-2 infection. Of note, apart from

metabolic conditions, similar morbidities were associated with KD, which may reflect overlapping and distinct pathophysiological features seen in both conditions. Clinical conditions

indicative of dysregulated or hyperinflammatory immune responses, such as severe (i.e., hospitalised) atopy, asthma and infection, occur more frequently in those who subsequently develop

KD.18 It is plausible that analogous mechanisms contribute to MIS-C susceptibility, as reflected in the pre-pandemic risk factors identified here, many of which are indicative of a

potentially dysregulated immune system.

Malignancy was associated with a substantially increased risk of MIS-C with worse outcomes; this was an interesting yet unexpected finding. When MIS-C was first recognised, it was

hypothesised that children with malignancy and immunosuppression would be more susceptible to developing the condition, but this has not been substantiated in previous reports.2,19 Although

the authors speculate that this novel finding could be due to misclassification of cytokine release syndrome due to chemotherapy as MIS-C, most children being treated for malignancy do not

have ongoing hypercytokinaemia that resembles MIS-C. Other factors, such as immune dysregulation arising from the underlying malignancy and its treatment, may provide alternative

explanations. Large datasets containing detailed clinical information on children with MIS-C, such as the one arising from the Best Available Treatment Study20 hold the potential for

exploring these findings in greater detail.

MIS-C was commoner in males, as previously reported, but intriguingly sex-stratified analyses showed that the associations with pre-pandemic risk factors were greater in females. This may

reflect the higher prevalence of some risk factors in females. Sex differences have been observed in other paediatric conditions, including infections,21 inflammatory disease22 and metabolic

conditions15,22 and are likely due to multiple factors including sex differences in immune and inflammatory responses,23 differences in hormone profiles, care-seeking behaviours, and sex

preferential treatment. Sex differences in clinical outcomes become increasingly apparent in late childhood and adolescence.24 Puberty has myriad impacts including physiological changes

resulting from hormonal changes, as well as psychosocial and behavioural changes; together these contribute to the observed sex differences in risk for immune-related and other conditions,

and to the poor outcomes seen in adolescents in several conditions.25,26,27 Sex differences and pubertal changes contributing to adverse disease outcomes are currently under-researched

areas. It is crucial to prioritise and allocate resources to investigate these factors to advance our understanding of their role and impact.

In addition to the identified risk factors for MIS-C, the study reported an important correlation between maternal and child health, with children of mothers hospitalised for COVID-19 having

a 24-fold increase in their own risk of hospitalisation for COVID-19. Extensive evidence supports the efficacy of vaccination in reducing COVID-19 severity in both adults and children, and

there is growing evidence for the role of vaccination in reducing the risk of MIS-C.28,29 In addition, the potential benefits of passive immunity through maternal vaccination30 highlight the

critical importance of comprehensive immunisation strategies to alleviate the disease burden associated with SARS-CoV-2 across all age groups.

The current study has highlighted one of the major benefits of big data studies in healthcare—extracting valuable knowledge from a large dataset that can be corroborated in subsequent

studies. Using large datasets is especially valuable for studying rare diseases; however, the number of children with MIS-C (n = 84) in this study was surprisingly low. This, together with

the large number of variables analysed, has the potential for false discovery, despite correction for multiple testing, and therefore results should be interpreted with caution, especially

given the imprecision of some of the estimates. The results of big data studies rely on the quality and content of the dataset. Total population data reduces bias arising from single-centre

studies, but inevitably comes at the cost of granularity of data and a lack of information on some important covariates. Auger et al. used data from hospital discharge summaries which

limited their cohort to children who had access to healthcare and who had co-morbidities which required hospitalisation; children with common co-morbidities (e.g., uncomplicated obesity,

mild asthma) who were not hospitalised were therefore not included in the analyses and it is not possible to comment on whether less severe but more prevalent co-morbid conditions increase

the risk of MIS-C. In addition, some co-morbidities, such as obesity and sleep disorders, are likely highly correlated, and distinguishing independent effects from these findings is not

possible. Last, using healthcare records also carries the risk of disease misclassification or missing data, as analysable data are reliant on accurate data entry and coding.

While this study did not use machine learning algorithms, the use of artificial intelligence (AI) is becoming increasingly common to help tackle the increasingly large volumes of data with

high dimensionality and to identify associations that may not be pre-specified in hypothesis-driven analyses. It is important to consider that AI-generated findings still require human

interpretation and vigorous scientific research to provide in-depth understanding.

During and following the COVID-19 pandemic, there has been a remarkable increase in collaborative efforts and data-sharing, which had advanced research at an accelerated pace. Collaborative,

multicentre, multidisciplinary research is essential to understand the contribution of genetics/epigenetics, environment and socioeconomic factors resulting in the disparities in

susceptibility to MIS-C and its severe complications.

HP is supported by the Diagnosis and Management of Febrile Illness using RNA Personalised Molecular Signature Diagnosis Study project grant. This project has received funding from the

European Union’s Horizon 2020 research and innovation programme under grant agreement No. 848196. EW is a co-investigator on the NIH-funded study: Diagnosing and Predicting Risk in Children

with SARS-CoV-2-Related Illness, Project Number: R61HD105590-01. DB is supported by a National Health and Medical Research Council Investigator Grant (GTN1175744). Research at Murdoch

Children’s Research Institute is supported by the Victorian Government’s Operational Infrastructure Support Program.

Department of Infectious Disease, Section of Paediatrics, Imperial College, London, UK

Infection and Immunity Theme, Murdoch Children’s Research Institute, Royal Children’s Hospital, Parkville, VIC, Australia

Department of Paediatrics, The University of Melbourne, Parkville, VIC, Australia

Paediatric Infectious Diseases, Imperial College Healthcare NHS Trust, London, UK

HP: conceptualisation, writing—original draft, writing—review and editing. EW: conceptualisation, writing—original draft, writing—review and editing, supervision. DB: conceptualisation,

writing—review and editing, supervision.

EW has provided investigator roles in relation to product development for AstraZeneca, Pfizer, Moderna, iLiAD and Sanofi with fees paid to Imperial College Healthcare NHS Trust. DB and HP

report no conflicts of interest.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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