You have full access to this article via your institution. Download PDF 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

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. REFERENCES *

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Article  CAS  PubMed  PubMed Central  Google Scholar  Download references FUNDING 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. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of Infectious Disease, Section of Paediatrics, Imperial College, London, UK Harsita Patel & Elizabeth Whittaker *

Infection and Immunity Theme, Murdoch Children’s Research Institute, Royal Children’s Hospital, Parkville, VIC, Australia David Burgner * Department of Paediatrics, The University of

Melbourne, Parkville, VIC, Australia David Burgner * Paediatric Infectious Diseases, Imperial College Healthcare NHS Trust, London, UK Elizabeth Whittaker Authors * Harsita Patel View author

publications You can also search for this author inPubMed Google Scholar * David Burgner View author publications You can also search for this author inPubMed Google Scholar * Elizabeth

Whittaker View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS 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. CORRESPONDING AUTHOR Correspondence to

David Burgner. ETHICS DECLARATIONS COMPETING INTERESTS 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. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional

claims in published maps and institutional affiliations. RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Patel, H., Burgner, D. & Whittaker, E.

Multisystem inflammatory syndrome in children: a longitudinal perspective on risk factors and future directions. _Pediatr Res_ 95, 15–17 (2024). https://doi.org/10.1038/s41390-023-02803-y

Download citation * Received: 18 June 2023 * Accepted: 09 August 2023 * Published: 04 September 2023 * Issue Date: January 2024 * DOI: https://doi.org/10.1038/s41390-023-02803-y SHARE THIS

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