Chronic disease incidence explained by stepwise models and co-occurrence among them

  1. Arróspide Elgarresta, Mikel 1
  2. Gerovska, Daniela 1
  3. Soto-Gordoa, Myrian 210
  4. Jauregui García, María L. 23
  5. Merino Hernández, Marisa L. 289
  6. Araúzo-Bravo, Marcos J. 14567
  1. 1 Computational Biology and Systems Biomedicine, Biogipuzkoa Health Research Institute, Calle Doctor Begiristain s/n, 20014 San Sebastian, Spain
  2. 2 Biogipuzkoa Health Research Institute, San Sebastian-Donostia, Spain
  3. 3 Tolosaldea Integrated Health Care Organization, Tolosa, Spain
  4. 4 Basque Foundation for Science, IKERBASQUE, Calle María Díaz Harokoa 3, 48013 Bilbao, Spain
  5. 5 CIBER of Frailty and Healthy Aging (CIBERfes), 28029 Madrid, Spain
  6. 6 Max Planck Institute for Molecular Biomedicine, Computational Biology and Bioinformatics, Röntgenstr. 20, 48149 Münster, Germany
  7. 7 Department of Cell Biology and Histology, Faculty of Medicine and Nursing, University of Basque Country (UPV/EHU), 48940 Leioa, Spain
  8. 8 Bidasoa Integrated Health Care Organization, Hondarribia, Spain
  9. 9 Research Network on Chronicity, Primary Care and Prevention and Health Promotion (RICAAPS), Kronikgune Group, Barakaldo, Spain
  10. 10 Mondragon University, Faculty of Engineering, Mondragon, Spain
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Revista:
iScience

ISSN: 2589-0042

Año de publicación: 2024

Volumen: 27

Número: 9

Páginas: 110816

Tipo: Artículo

DOI: 10.1016/J.ISCI.2024.110816 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: iScience

Resumen

Multimorbidity (MM) is the co-occurrence of two or more chronic diseases. We provided a dynamic approach revealing the MM complexity constructing a multistep incidence-age model for all patients with MM between 2014 and 2021 in the Basque Health System, Spain. The multistep model, with eight steps for males and nine for females, is a very well-fitting representation of MM. To gain insight into the MM components, we modeled the 19 diseases used to calculate the Charlson Comorbidity Index (CCI). We observed that the CCI diseases formed a complex interaction network. Hierarchical clustering of the incidence-age profiles clustered the CCI diseases into low- and high-risk of dying pathologies. Diseases with a higher number of steps are better represented by a multistep model. Anatomically, diseases associated with the central nervous system have the highest number of steps, followed by those associated with the kidney, heart, peripheral vasulature, pancreas, joints, cerebral vasculature, lung, stomach, and liver.

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Información de financiación

This project has received funding from the European Union’s Horizon 2020 Research and Innovation programme under grant agreement N° 899417, by Ministerio de Ciencia e Innovación, Spain Grant No. PID2020-119715GB-I00/AEI/10.13039/501100011033, and by Instituto de Salud Carlos III, Infrastructure of Precision Medicine associated with Science and Technology (IMPaCT) of the Strategic Action in Health (iDATA-MP).

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