Heterogeneity in paediatric diabetes clinical presentation and its link to disease severity
Background Diabetes subtypes with different clinical profiles have been found and replicated in adults. However, clinical heterogeneity of paediatric-onset diabetes remains unexplored. Better capture of different aetiologies of the disease could prove a powerful tool towards precision medicine. Methods We performed data-driven clustering analysis in patients with newly diagnosed diabetes (n = 3,064) from the Norwegian Childhood Diabetes Registry. Patients were stratified by autoantibody status and clustered based on five clinical variables: Age at diagnosis, fasting glucose, HbA1c, fasting C-peptide Z-score, and BMI Z-score. We assessed inter-cluster differences regarding severity of disease, early treatment needs, polygenic scores (PS), and serum biomarkers. Findings We identified two clusters of autoantibody-positive and three clusters of autoantibody-negative patients: 1) Childhood severe autoimmune diabetes (CSAID; n = 1482, 48.37 %), characterized by childhood-onset diabetes, milder disease presentation, unaltered lipidic profiles, higher Type 1 diabetes PS, and autoantibody positivity; 2) Adolescence severe autoimmune diabetes (ASAID; n = 1252, 40.86 %) with adolescent-onset of diabetes, more severe disease presentation, higher diabetic ketoacidosis, lipidic profiles signaling towards prolonged metabolic disease, and autoantibody positivity; 3) Childhood severe insulin-deficient diabetes (CSIDD; n = 106, 3.46 %) and 4) Adolescence severe insulin-deficient diabetes (ASIDD; n = 144, 4.70 %) with similar characteristics to CSAID and ASAID, respectively, but no autoantibody positivity; and 5) Adolescence severe insulin-resistant diabetes (ASIRD; n = 80, 2.61 %), the oldest group, with the highest C-peptide, BMI Z-score, and Type 2 diabetes PS. CSAID and ASAID presented similarities to previously described endotypes for type 1 diabetes. Interpretation We grouped patients with paediatric diabetes into five subgroups with varying clinical severity, genetic risk, and metabolic profiles. Similarities between autoantibody-positive and -negative clusters underscore the importance of adopting a personalised, multivariate approach to diabetes management that extends beyond autoantibody status. We hypothesise that our clusters may be connected to previously described endotypes of type 1 diabetes, facilitating patient classification without the need for pancreatic biopsies. Further understanding of this concept could help define the mechanisms involved in disease initiation, time to diagnosis, and progression.
### Competing Interest Statement
The authors have declared no competing interest.
### Clinical Protocols
[https://github.com/ma-juarez/Pediatric\_Diabetes\_Clustering][1]
### Funding Statement
This study was supported by the Research Council of Norway (#301178), the European Research Council (#101171420), and the University of Bergen.
### Author Declarations
I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes
The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
Informed consent was obtained from all registry participants. The administrative board of the Norwegian Childhood Diabetes Registry approved the study protocol. The establishment of the Norwegian Childhood Diabetes Registry and initial data collection was based on a license from the Norwegian Data Protection Agency and approval from The Regional Committee for Medical Research Ethics. The Norwegian Childhood Diabetes Registry cohort is currently regulated by the Norwegian Health Registry Act. The study was approved by the ethical committee REK vest, reference 18836, University of Bergen, Norway.
I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.
Yes
I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).
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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.
Yes
Data from the Norwegian Childhood Diabetes Registry can be made available to researchers, provided approval from the Regional Committees for Medical and Health Research Ethics (REC), compliance with the EU General Data Protection Regulation (GDPR) and approval from the data owners. The consent given by the participants does not open for storage of data on an individual level in repositories or journals. Researchers who want access to data sets for replication should apply to the registry. Access to data sets requires approval from The Regional Committee for Medical and Health Research Ethics in Norway and an agreement with the Norwegian Childhood Diabetes Registry (www.oslodiabetes.no/childhood).
[1]: https://github.com/ma-juarez/Pediatric_Diabetes_Clustering
knoppix
Thomas Lin Pedersen