Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

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

Nature Medicine (2024)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

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).

References

  1. Penrose, L. S. et al. A Clinical and Genetic Study of 1280 Cases of Mental Defect (HMSO, 1938).

  2. Sarason, S. B. Psychological Problems in Mental Deficiency (Harper, 1949).

  3. Yannet, H. Diagnostic classification of patients with mental deficiency: distribution of 1,330 institutionalized patients, with review of incidence of convulsive disorders and noncerebral developmental anomalies. Am. J. Dis. Child. 70, 83–88 (1945).

    Article  Google Scholar 

  4. Kallmann, F. J. Heredity in Health and Mental Disorder; Principles of Psychiatric Genetics in the Light of Comparative Twin Studies (Norton, 1953).

  5. Pitt, D. & Roboz, P. A survey of 782 cases of mental deficiency. J. Ment. Defic. Res. 9, 4–23 (1965).

    CAS  PubMed  Google Scholar 

  6. Gustavson, K. H., Holmgren, G., Jonsell, R. & Son Blomquist, H. K. Severe mental retardation in children in a northern Swedish county. J. Ment. Defic. Res. 21, 161–180 (1977).

    CAS  PubMed  Google Scholar 

  7. Laxova, R., Ridler, M. A. & Bowen-Bravery, M. An etiological survey of the severely retarded Hertfordshire children who were born between January 1, 1965 and December 31, 1967. Am. J. Med. Genet. 1, 75–86 (1977).

    Article  CAS  PubMed  Google Scholar 

  8. Göstason, R. Psychiatric illness among the mentally retarded. A Swedish population study. Acta Psychiatr. Scand. Suppl. 318, 1–117 (1985).

    Article  PubMed  Google Scholar 

  9. Reid, A. H. Schizophrenia in mental retardation: clinical features. Res. Dev. Disabil. 10, 241–249 (1989).

    Article  CAS  PubMed  Google Scholar 

  10. Deb, S. & Joyce, J. Psychiatric illness and behavioural problems in adults with learning disability and epilepsy. Behav. Neurol. 11, 125–129 (1999).

    Article  CAS  PubMed  Google Scholar 

  11. Holland, T. & Murphy, G. Behavioural and psychiatric disorder in adults with mild learning difficulties. Int. Rev. Psychiatry 2, 117–135 (1990).

    Article  Google Scholar 

  12. Mirabi, M. in Child and Adolescent Psychiatry, Mental Retardation and Geriatric Psychiatry (eds Pichot, P. et al.) 177–181 (Springer, 1985).

  13. Corbett, J. A. in Scientific Studies in Mental Retardation (eds Dobbing, J. et al.) 479–506 (Palgrave Macmillan, 1984).

  14. Linna, S. L. et al. Psychiatric symptoms in children with intellectual disability. Eur. Child Adolesc. Psychiatry 8, 77–82 (1999).

    Article  PubMed  Google Scholar 

  15. Farag, T. I. et al. Disease profile of 400 institutionalized mentally retarded patients in Kuwait. Clin. Genet. 44, 329–334 (1993).

    Article  CAS  PubMed  Google Scholar 

  16. Bratek, A., Krysta, K. & Kucia, K. Psychiatric comorbidity in older adults with intellectual disability. Psychiatr. Danub. 29, 590–593 (2017).

    PubMed  Google Scholar 

  17. McGrother, C. W. et al. Epilepsy in adults with intellectual disabilities: prevalence, associations and service implications. Seizure 15, 376–386 (2006).

    Article  PubMed  Google Scholar 

  18. Daily, D. K., Ardinger, H. H. & Holmes, G. E. Identification and evaluation of mental retardation. Am. Fam. Physician 61, 1059–1067 (2000).

    CAS  PubMed  Google Scholar 

  19. Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Byrd, J. B., Greene, A. C., Prasad, D. V., Jiang, X. & Greene, C. S. Responsible, practical genomic data sharing that accelerates research. Nat. Rev. Genet. 21, 615–629 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Robinson, P. N. et al. The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease. Am. J. Hum. Genet. 83, 610–615 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Köhler, S. et al. The human phenotype ontology in 2021. Nucleic Acids Res. 49, D1207–D1217 (2021).

    Article  PubMed  Google Scholar 

  23. Firth, H. V. et al. DECIPHER: Database of Chromosomal Imbalance and Phenotype in Humans using Ensembl Resources. Am. J. Hum. Genet. 84, 524–533 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Boyd, P. A. et al. Paper 1: the EUROCAT network—organization and processes. Birth Defects Res. A 91, S2–S15 (2011).

    Article  CAS  Google Scholar 

  25. Dolk, H. EUROCAT: 25 years of European surveillance of congenital anomalies. Arch. Dis. Child. Fetal Neonatal Ed. 90, F355–F358 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Greenlees, R. et al. Paper 6: EUROCAT member registries: organization and activities. Birth Defects Res. A 91, S51–S100 (2011).

    Article  CAS  Google Scholar 

  27. McKusick, V. A. Mendelian Inheritance in Man and its online version, OMIM. Am. J. Hum. Genet. 80, 588–604 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Nabais Sá, M. J. et al. De novo and biallelic DEAF1 variants cause a phenotypic spectrum. Genet. Med. 21, 2059–2069 (2019).

    Article  PubMed  Google Scholar 

  29. Fokkema, I. F. A. C. et al. LOVD v.2.0: the next generation in gene variant databases. Hum. Mutat. 32, 557–563 (2011).

    Article  CAS  PubMed  Google Scholar 

  30. Adler, A. et al. PhenoDis: a comprehensive database for phenotypic characterization of rare cardiac diseases. Orphanet J. Rare Dis. 13, 22 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Li, B. et al. GPCards: an integrated database of genotype-phenotype correlations in human genetic diseases. Comput. Struct. Biotechnol. J. 19, 1603–1611 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Mungall, C. J. et al. The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species. Nucleic Acids Res. 45, D712–D722 (2017).

    Article  CAS  PubMed  Google Scholar 

  33. Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Fuller, Z. L., Berg, J. J., Mostafavi, H., Sella, G. & Przeworski, M. Measuring intolerance to mutation in human genetics. Nat. Genet. 51, 772–776 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Lelieveld, S. H. et al. Meta-analysis of 2,104 trios provides support for 10 new genes for intellectual disability. Nat. Neurosci. 19, 1194–1196 (2016).

    Article  CAS  PubMed  Google Scholar 

  36. Kaplanis, J. et al. Evidence for 28 genetic disorders discovered by combining healthcare and research data. Nature 586, 757–762 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Dingemans, A. J. M. et al. Human disease genes website series: an international, open and dynamic library for up-to-date clinical information. Am. J. Med. Genet. A 185, 1039–1046 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Page, M. J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Int. J. Surg. 88, 105906 (2021).

    Article  PubMed  Google Scholar 

  39. Bokeh Development Team. Bokeh: Python Library for Interactive Visualization https://bokeh.pydata.org/en/latest/ (2021).

  40. Manders, P., Lutomski, J. E., Smit, C., Swinkels, D. W. & Zielhuis, G. A. The Radboud Biobank: a central facility for disease-based biobanks to optimise use and distribution of biomaterial for scientific research in the Radboud University Medical Center, Nijmegen. Open J. Bioresour. 5, 2 (2018).

    Article  Google Scholar 

  41. Richards, S. et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. 17, 405–424 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Daoud, J. Multicollinearity and regression analysis. J. Phys. Conf. Ser. 949, 012009 (2017).

    Article  Google Scholar 

  43. Dingemans, L. ldingemans/PhenomAD_NDD_data: original release as in the paper. Zenodo https://doi.org/10.5281/zenodo.10908427 (2024).

Download references

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.

Author information

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

Authors
  1. Alexander J. M. Dingemans
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sandra Jansen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jeroen van Reeuwijk
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nicole de Leeuw
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rolph Pfundt
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Janneke Schuurs-Hoeijmakers
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Bregje W. van Bon
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Carlo Marcelis
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Charlotte W. Ockeloen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Marjolein Willemsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Pleuntje J. van der Sluijs
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Gijs W. E. Santen
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. R. Frank Kooy
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Anneke T. Vulto-van Silfhout
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Tjitske Kleefstra
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. David A. Koolen
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Lisenka E. L. M. Vissers
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Bert B. A. de Vries
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

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.

Additional information

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

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.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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 (2024). https://doi.org/10.1038/s41591-024-03005-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41591-024-03005-7

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing