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Prevalence of comorbidities in individuals with neurodevelopmental disorders from the aggregated phenomics data of 51,227 pediatric individuals

Nature Medicine volume 30, pages 1994–2003 (2024)Cite this article

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Abstract

The prevalence of comorbidities in individuals with neurodevelopmental disorders (NDDs) is not well understood, yet these are important for accurate diagnosis and prognosis in routine care and for characterizing the clinical spectrum of NDD syndromes. We thus developed PhenomAD-NDD, an aggregated database containing the comorbid phenotypic data of 51,227 individuals with NDD, all harmonized into Human Phenotype Ontology (HPO), with in total 3,054 unique HPO terms. We demonstrate that almost all congenital anomalies are more prevalent in the NDD population than in the general population, and the NDD baseline prevalence allows for an approximation of the enrichment of symptoms. For example, such analyses of 33 genetic NDDs show that 32% of enriched phenotypes are currently not reported in the clinical synopsis in the Online Mendelian Inheritance in Man (OMIM). PhenomAD-NDD is open to all via a visualization online tool and allows us to determine the enrichment of symptoms in NDD.

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Fig. 1: PRISMA 2020 flow diagram.
Fig. 2: Prevalence of kidney-related HPO terms in PhenomAD-NDD.
Fig. 3: Visualization of the data in PhenomAD-NDD.
Fig. 4: Subgroup analyses.

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Data availability

The contents of PhenomAD-NDD are freely available online at https://humandiseasegenes.nl/phenomadndd/, as well as a tool to calculate the enrichment of symptoms for your own dataset. The summarized data are available at https://github.com/ldingemans/PhenomAD_NDD_data/. PubMed (https://pubmed.ncbi.nlm.nih.gov/) was used for the literature search in this study.

Code availability

If the tools provided online are insufficient, or if others would like to implement the enrichment analyses themselves, all code and summarized data used and developed in this study are available at https://github.com/ldingemans/PhenomAD_NDD_data/ (ref. 43).

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Acknowledgements

We are grateful to the Dutch Organisation for Health Research and Development for ZON-MW grant no. 912-12-109 (to B.B.A.d.V. and L.E.L.M.V.), Donders Junior researcher grant no. 2019 (to B.B.A.d.V. and L.E.L.M.V.) and Aspasia grant no. 015.014.066 (to L.E.L.M.V.). The aims of this study contribute to the Solve-RD project (to L.E.L.M.V.), which has received funding from the European Union’s Horizon 2020 Research and Innovation program under grant agreement no. 779257. Multiple authors of this publication are members of the European Reference Network on Rare Congenital Malformations and Rare Intellectual Disability ERN-ITHACA (EU Framework Partnership Agreement ID: 3HP-HPFPA ERN-01-2016/739516). The funder(s) of the study had no role in study design, data collection, data analysis, data interpretation or writing of the report.

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Authors and Affiliations

  1. Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands

    Alexander J. M. Dingemans, Sandra Jansen, Jeroen van Reeuwijk, Nicole de Leeuw, Rolph Pfundt, Janneke Schuurs-Hoeijmakers, Bregje W. van Bon, Carlo Marcelis, Charlotte W. Ockeloen, Marjolein Willemsen, Anneke T. Vulto-van Silfhout, Tjitske Kleefstra, David A. Koolen, Lisenka E. L. M. Vissers & Bert B. A. de Vries

  2. Department of Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands

    Pleuntje J. van der Sluijs & Gijs W. E. Santen

  3. Department of Medical Genetics, University of Antwerp, Antwerp, Belgium

    R. Frank Kooy

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Contributions

This research included worldwide researchers who were involved throughout the research process and each had their own role and responsibility. The study was designed by L.E.L.M.V. and B.B.A.d.V., and implemented by A.J.M.D., L.E.L.M.V. and B.B.A.d.V. The data are owned by the various independent researchers (A.J.M.D., S.J., J.v.R., N.d.L., R.P., J.S.-H., B.W.v.B., C.M., C.W.O., M.W., P.J.v.d.S., G.W.E.S., R.F.K., A.T.V.-v.S., T.K., D.A.K., L.E.L.M.V. and B.B.A.d.V.), whereas the intellectual property lies with the leading research group from the Radboud University Medical Center. The original draft was written by A.J.M.D., L.E.L.M.V. and B.B.A.d.V. All authors were involved in reviewing and editing the paper.

Corresponding author

Correspondence to Bert B. A. de Vries.

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Nature Medicine thanks Kristopher Kahle and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Sonia Muliyil, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 Countries contributing to EUROCAT.

The 18 countries whose institutions contribute data to the EUROCAT registry, with their relative contribution in various shades of blue. Of note, the total number of individuals in the EUROCAT registry is 11,616,332.

Extended Data Fig. 2 Distribution of number of clinical features.

The distribution of the HPO terms of the individuals seen at the outpatient clinic in the Radboud University Medical Center.

Extended Data Fig. 3 Result of enrichment analysis.

A) The results of the top 10 results of the enrichment analysis for two of the 33 investigated genetic syndromes are shown here: MRD50 (OMIM #617787), for which 16% of the symptoms are described in the clinical synopsis in OMIM, and NEDHELS (OMIM #617171, 100% concordance). Shown are the 10 clinical features that are the most enriched: that is with the highest ratio when dividing the prevalence in the syndrome with the prevalence in PhenomAD-NDD. The size of the nodes in the network corresponds to the prevalence in that specific genetic syndrome. The phenotypic data of the investigated syndromes was gathered from the specific website of the Human Disease Gene website series, with the prevalence rate in the syndrome referring to the proportion of individuals with the specific genetic syndrome who exhibit a particular clinical feature. The color corresponds to the enrichment ratio, which compares the prevalence in the syndrome to the prevalence in PhenomAD-NDD. To avoid spurious enrichments, only clinical features with a prevalence of at least 1% are shown in this figure. Using the developed tool in either the electronic supplement or online at the Human Disease Genes website series (https://humandiseasegenes.nl/phenomadndd/)6, others are able to determine enriched clinical features and generate these figures for their syndrome of interest. B) The clinical synopsis of OMIM8 as shown for these two genetic syndromes.

Supplementary information

Reporting Summary

Supplementary Table 1

The search strategy of the literature search and meta-analysis, including all included and excluded studies and data collected per study.

Supplementary Table 2

The results of the enrichment analysis for 33 genetic disorders.

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Dingemans, A.J.M., Jansen, S., van Reeuwijk, J. et al. Prevalence of comorbidities in individuals with neurodevelopmental disorders from the aggregated phenomics data of 51,227 pediatric individuals. Nat Med 30, 1994–2003 (2024). https://doi.org/10.1038/s41591-024-03005-7

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