Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/48616
Title: Telomere length in patients with Marfan Syndrome
Authors: Tournoy, Tijs K.
D'hulst, Simon
Demolder, Anthony
Derudder, Robbe
MARTENS, Dries 
Mosquera, Laura Muiño
Coucke, Paul
De Backer, Julie
Issue Date: 2026
Publisher: Elsevier
Source: International Journal of Cardiology, 450 (Art N° 134234)
Abstract: Background: Marfan syndrome (MFS) is a multisystemic heritable thoracic aortic disease entity characterized by progressive aortic dilatation and life-threatening cardiovascular complications. Chronic inflammation and oxidative stress are increasingly recognized in its pathophysiology, and are important drivers of telomere shortening, a hallmark of biological aging. We hypothesized that adults with MFS have shorter telomere length (TL) compared to healthy controls. Methods: Relative average leukocyte TL was measured in 59 adults with molecularly confirmed MFS (median age 38 years, 29 females) and 59 age-and sex-matched healthy controls. TL was determined by a singleplex qPCR assay. Results: Patients with MFS had shorter TL compared to healthy controls (0.99 ± 0.19 vs. 1.07 ± 0.21, p = 0.033). In univariate analysis, we found that major adverse cardiovascular events (defined as aortic dissection, arrhythmia or heart failure) were associated with shorter TL (β = 0.168, 95%CI-0.291; 0.013, p = 0.008). No other clinical or genetic variables showed significant associations in either the raw or age-and sex-adjusted TL analyses. Conclusion: Adults with MFS have shorter leukocyte TL, and an association was found between shorter TL and severe cardiovascular events. These findings suggest a role for accelerated aging mechanisms in the patho-physiology of the disease.
Keywords: Marfan syndrome;Heritable thoracic aortic disease;Telomere length;Telomere attrition;Aging
Document URI: http://hdl.handle.net/1942/48616
ISSN: 0167-5273
e-ISSN: 1874-1754
DOI: 10.1016/j.ijcard.2026.134234
Rights: 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Category: A1
Type: Journal Contribution
Appears in Collections:Research publications

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