Erythrocyte membrane fluidity: A novel biomarker of residual cardiovascular risk in type 2 diabetes

Giada Bianchetti, Chiara Maria Assunta Cefalo, Carla Ferreri, Anna Sansone, Marilena Vitale, Cassandra Serantoni, Alessio Abeltino, Teresa Mezza, Pietro Manuel Ferraro, Marco De Spirito, Gabriele Riccardi, Andrea Giaccari*, Giuseppe Maulucci

*Autore corrispondente per questo lavoro

Risultato della ricerca: Contributo in rivistaArticolo in rivista

Abstract

Aims: Improving the composition of circulating fatty acids (FA) leads to a reduction in cardiovascular diseases (CVD) in high-risk individuals. The membrane fluidity of red blood cells (RBC), which reflects circulating FA status, may be a valid biomarker of cardiovascular (CV) risk in type 2 diabetes (T2D). Methods: Red blood cell membrane fluidity, quantified as general polarization (GP), was assessed in 234 subjects with T2D, 86 with prior major CVD. Based on GP distribution, a cut-off of.445 was used to divide the study cohort into two groups: the first with higher GP, called GEL, and the second, defined as lower GP (LGP). Lipidomic analysis was performed to evaluate FA composition of RBC membranes. Results: Although with comparable CV risk factors, the LGP group had a greater percentage of patients with major CVD than the GEL group (40% vs 24%, respectively, p <.05). Moreover, in a logistic regression analysis, a lower GP value was independently associated with the presence of macrovascular complications. Lipidomic analysis showed a clear shift of LGP membranes towards a pro-inflammatory condition due to higher content of arachidonic acid and increased omega 6/omega 3 index. Conclusions: Increased membrane fluidity is associated with a higher CV risk in subjects with T2D. If confirmed in prospective studies, membrane fluidity could be a new biomarker for residual CV risk assessment in T2D.
Lingua originaleEnglish
pagine (da-a)N/A-N/A
RivistaEuropean Journal of Clinical Investigation
Volume54
DOI
Stato di pubblicazionePubblicato - 2024

Keywords

  • cardiovascular disease
  • cardiovascular risk assessment
  • fatty acids
  • fluorescence microscopy
  • machine-learning
  • membrane fluidity
  • type 2 diabetes

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