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Shared and specific blood biomarkers for multimorbidity

  • Alice Margherita Ornago
  • , Caterina Gregorio
  • , Federico Triolo
  • , Ann Zenobia Moore
  • , Alessandra Marengoni
  • , Giorgi Beridze
  • , Giulia Grande
  • , Giuseppe Bellelli
  • , Matilda Dale
  • , Claudia Fredolini
  • , Luigi Ferrucci
  • , Laura Fratiglioni
  • , Amaia Calderón-Larrañaga
  • , Davide Liborio Vetrano*
  • *Corresponding author for this work
  • University of Milan - Bicocca
  • Karolinska Institutet
  • National Institutes of Health
  • University of Brescia
  • Stockholm Gerontology Research Center
  • Azienda Ospedaliera San Gerardo Monza
  • KTH Royal Institute of Technology

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Aging is accompanied by the progressive accumulation of biological deficits, which increases susceptibility to developing multiple chronic diseases (that is, multimorbidity). The biological underpinnings of multimorbidity remain poorly understood. Here we analyzed 54 blood biomarkers reflecting inflammatory, vascular, metabolic and neurodegenerative processes in 2,247 individuals aged 60 and over from the Swedish National Study on Aging and Care in Kungsholmen. Multimorbidity was assessed using three measures: baseline total disease count, baseline multimorbidity patterns identified through latent class analysis and 15-year rate of disease accumulation. Associations between baseline biomarkers and multimorbidity measures were examined using least absolute shrinkage and selection operator regression. Growth differentiation factor 15, hemoglobin A1c, cystatin C, leptin and insulin were consistently and positively associated with all multimorbidity measures. Additional biomarkers demonstrated specific associations with distinct multimorbidity patterns. Moreover, faster disease accumulation was directly associated with gamma-glutamyl transferase and inversely with albumin. Longitudinal results were externally validated in 522 participants from the Baltimore Longitudinal Study of Aging, with comparable predictive accuracy. Our findings suggest that multiple biological processes contribute to multimorbidity through shared and distinct mechanisms. Metabolic disturbances emerged as a key driver of multimorbidity. If confirmed, these processes could represent targets for interventions to mitigate disease accumulation.

Original languageEnglish
Pages (from-to)736-745
Number of pages10
JournalNature medicine
Volume32
Issue number2
DOIs
Publication statusPublished - Feb 2026

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