PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 8, 2026European Heart Journal0 citations

Fatigue and its key determinants in marfan and ehlers-danlos syndromes: cluster analysis from an italian multicentre study

View Full Paper
NUNathasha Samali UdugampolageMAM A AngolaniJTJ T Taurino

Key Points

  • The study aims to identify patient clusters based on fatigue and its determinants in Marfan and Ehlers-Danlos syndromes.
  • Conducted a cross-sectional multicentre study
  • Utilized hierarchical cluster analysis with Ward's method
  • Applied validated self-report questionnaires including FSS, PHQ-9, and ISI
  • Employed t-distributed stochastic neighbor embedding (t-SNE) for data reduction
  • Assessed clusters using silhouette statistics
  • Identified two distinct clusters for Marfan syndrome: 'Young and Moderately Fatigued' and 'Older and Fatigued'
  • Identified two clusters for Ehlers-Danlos syndrome: 'Young and Highly Fatigued' and 'Older and Severely Fatigued'
  • Patients with EDS exhibited more severe fatigue compared to those with MFS
  • Depressive symptoms significantly correlated with fatigue levels in MFS patients

Abstract

Abstract Background Depression, insomnia and psychotherapy participation are key factors affecting fatigue severity in Marfan syndrome (MFS) and Ehlers-Danlos syndrome (EDS), heritable connective tissue disorders with systemic effects and high cardiovascular morbidity (1,2). Nurses play a crucial role in recognizing these factors, providing patient education and facilitating access to appropriate care to improve fatigue management and overall well-being (3). Current research lacks comprehensive epidemiological data, underscoring the need for further investigation to determine patient clusters according to fatigue and its key determinants. Purpose This study identified clusters in MFS and EDS populations based on demographic and clinical data, fatigue levels, physical and psychological aspects. Methods Hierarchical cluster analysis with Ward’s method performed on data from a cross-sectional multicentre study using validated self-report Likert scale questionnaires (Fatigue Severity Scale-FSS, Patient Health Questionnaire 9-PHQ-9; Insomnia Severity Index Scale-ISI) and health assessments. Information derived from data was reduced into 2 stochastic variables using t-distributed stochastic neighbour embedding (t-SNE). Cluster analysis was assessed employing silhouette statistics. Results Two distinct clusters were identified for each condition. For MFS: The "Young and Moderately Fatigued" cluster (mean age 24.44 ± 5.04 years; mean age at diagnosis 11.96 ± 8.13 years; mean BMI 19.33 ± 3.44; mean FSS score 3.48 ± 1.68; mean PHQ-9 score 5.27 ± 4.27) represents individuals with a moderate illness history and fatigue levels. "Older and Fatigued" cluster (mean age 48.67 ± 8.7 years; mean age at diagnosis 18.81 ± 12.59 years; mean BMI 23.04 ± 3.87; mean FSS score 4.16 ± 1.69; mean PHQ-9 score 6.52 ± 4.62) reports higher BMI and longer disease history. For EDS: The "Young and Highly Fatigued" cluster (mean age 26.35 ± 3.16 years; mean age at diagnosis 7.27 ± 6.63 years; mean BMI 20.96 ± 2.77; mean FSS score 5.17 ± 1.49; mean PHQ-9 score 11.20 ± 6.59) outlines recent illness history but elevated fatigue and depression. "Older and Severely Fatigued" cluster (mean age 47.25 ± 8.5 years; mean age at diagnosis 10.98 ± 11.14 years; mean BMI 23.63 ± 5.61; mean FSS score 5.61 ± 1.44; mean PHQ-9 score 12.06 ± 5.63) underline a prolonged illness history, higher BMI and severe fatigue. Conclusion Fatigue is more severe in EDS whereas in subjects with MFS is notably linked to depressive symptoms. The increased fatigue and depression observed in older individuals highlight the importance of a multidisciplinary approach in nursing care and the need for tailored interventions to address the patients’ complex needs.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Udugampolage et al. (2025) studied this question.

synapsesocial.com/papers/698827c90fc35cd7a8846bc1https://doi.org/10.1093/eurheartj/ehaf784.4347
Ask AI
Helpful
Bookmark
Share
View Full Paper