4.6 Article

A genetic model for multimorbidity in young adults

期刊

GENETICS IN MEDICINE
卷 22, 期 1, 页码 132-141

出版社

ELSEVIER SCIENCE INC
DOI: 10.1038/s41436-019-0603-1

关键词

noncommunicable diseases; drug therapy; multiple chronic conditions; rare genetic disorders; DiGeorge syndrome

资金

  1. Canadian Institutes of Health Research [97800, 111238]
  2. Dalglish Chair award
  3. Dalglish Fellow award
  4. Canada Research Chairs program
  5. Mr. Rajeeb Mukherjee BSc (Hons) (London School of Economics) Fellowship in Schizophrenia
  6. McLaughlin Centre Accelerator Grant
  7. National Institute of Mental Health [U01 MH101723-01(3/5)]
  8. W. Garfield Weston Foundation

向作者/读者索取更多资源

Purpose Multimorbidity is increasing in younger adults but is understudied in this population. We used 22q11.2 deletion syndrome (22q11.2DS) as a genetic model to investigate multimorbidity in young to middle-aged adults. Methods Using the Anatomical Therapeutic Chemical (ATC) Classification System and setting five or more concurrent prescription medications as a proxy for multimorbidity, we compared data on 264 adults with 22q11.2DS (median age 27.8, range 17.3-68.3 years) with that for a community-based Canadian general population sample (n = 25,287). We used logistic regression to examine possible predictors of multimorbidity in 22q11.2DS. Results Multimorbidity in 22q11.2DS in the 25-44 year age group (34.7%) was significantly more prevalent than in the general population, both for the same age group (2.9%, prevalence ratio [PR] = 11.9, 95% CI 8.4-17.1) and compared with those aged 45-64 years (16.4%, PR = 2.1, 95% CI 1.6-2.7). Neuropsychiatric and endocrinological medication classes predominated. Within 22q11.2DS, older age and psychotic illness, but not sex, major congenital heart disease, or intellectual disability, were significant predictors of multimorbidity. Conclusion The results indicate that adults with 22q11.2DS have a significant burden of illness with levels of multimorbidity comparable with those of the general population several decades older. In younger adults with multimorbidity, certain disease patterns may help identify genetic disorders in big data.

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