Abstract 4131618: Association between Clinical Characteristics and Telomere Length - Factor Analysis of Mixed Data (FAMD)-based Cluster Analysis
Introduction: Telomere length is a marker of cellular aging and is correlated with various cardiovascular risk factors. Aim: To assess the association between clinical characteristics of patients and telomere length in an unselected cohort of patients under the care of primary care physicians. Methods: The LIPIDOGEN study was carried out in the primary care 2015. Patients recruited to LIPIDOGEN cohort (n=1788) were a random subset of patients of LIPIDOGRAM 2015 (n=13,724) study. Patients for LIPIDOGRAM2015/LIPIDOGEN study were recruited in all 16 administrative regions in Poland. Each patient was asked to fill the questionnaire on risk factors, chronic, treatment and lifestyle and also had saliva secured for DNA analyses. Factor Analysis for Mixed Data (FAMD) based cluster analysis was utilized to discern groups of patients with similar clinical profiles and correlate them with relative telomere length (rTL). RTL was measured using quantitative polymerase chain reaction. Results: DNA quality was sufficient for further analyses in 1606 patients. After outlier exclusion, 1,342 patients (59.3% female, mean age 50.2 years) were analyzed. FAMD revealed two distinct clinical characteristic patterns, explaining 41.6% of the total variance in the clinical variables. The first pattern, correlated with male gender and a higher prevalence of MetS, hypertension, diabetes, obesity, greater waist circumference, elevated non-HDL-C and remnant-C levels, was linked to shorter rTL (r = -0.13, p < 0.001). The second pattern, characterized mainly by lipid parameters, showed no significant association with comorbidities or rTL (r = -0.04, p = 0.14). Conclusions: Pattern of clinical characteristics marked by classical cardiovascular risk factors, is inversely related to telomere length, underlining the potential role of metabolic disturbances in cellular aging. The second lipid-centric pattern without significant comorbidities was not associated with telomere length.
Authors
- Natalia Pawlas (ORCID: https://orcid.org/0000-0002-7551-9371)
- Marcin Goławski
- Tadeusz Osadnik (ORCID: https://orcid.org/0000-0002-3202-6972)
- Anna Pańczyszyn (ORCID: https://orcid.org/0000-0003-1580-3925)
- Agnieszka Gach (ORCID: https://orcid.org/0000-0002-6590-5797)
- Kamila Osadnik (ORCID: https://orcid.org/0000-0002-4618-9844)
- Martyna Fronczek (ORCID: https://orcid.org/0000-0003-3669-1416)
- Joanna Katarzyna Strzelczyk (ORCID: https://orcid.org/0000-0002-3686-5685)
- Maciej Banach (ORCID: https://orcid.org/0000-0001-6690-6874)
- Mateusz Lejawa
- Marek Gierlotka (ORCID: https://orcid.org/0000-0001-5639-2128)
- Ewa Boniewska‐Bernacka (ORCID: https://orcid.org/0000-0002-4424-8409)
- Anna Goc (ORCID: https://orcid.org/0000-0003-4271-1954)
- Jacek Jerzy Jozwiak (ORCID: https://orcid.org/0000-0002-2645-7274)
- Karol Krystek (ORCID: https://orcid.org/0000-0002-9951-8218)
Institutions
- Medical University of Silesia (PL)
- University of Opole (PL)
- Medical University of Lodz (PL)
- Polish Mother’s Memorial Hospital Research Institute (PL)
- University of Silesia in Katowice (PL)
Publication Details
- Journal
- Circulation
- Published
- 2024-11-12
- DOI
- https://doi.org/10.1161/circ.150.suppl_1.4131618
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00