Latent Profiles of Modifiable Biological Factors and Their Associations with Lifestyle Factors and Cardiovascular
Giedre Aukstakalniene1, Renata Paukstaitiene2, Abdonas Tamosiunas1
1Laboratory of Population Studies, Institute of Cardiology, Medical Academy, Lithuanian University of Health Sciences, Sukilėlių Ave. 15, 50103 Kaunas, Lithuania.
Abstract:
Background/Objectives: There is growing interest among researchers in improved biological risk factor indices or combinations of indices, which have emerged as a major focus in evaluating the risk of cardiovascular disease (CVD). This study aimed to identify latent profiles based on the clustering of biological factors and to explore associations between lifestyle factors and CVD outcomes across the identified latent profiles. Methods: This epidemiological health survey was performed during 2023-2024. A random sample of Kaunas inhabitants aged 25-69 years, stratified by sex and age, was randomly selected from the Lithuanian population register. The 3426 individuals were screened. Latent profile analysis was performed using Latent Gold 6.1 and IBM SPSS Statistics 30. Multinomial logistic regression and multivariable binary logistic regression were used to evaluate the associations between biological risk factor profiles, lifestyle factors, and CVD outcome. Results: Three biological risk factor profiles were identified: low-risk profile (42.6%) was considered the healthiest, having the lowest levels of body mass index (BMI), fasting glucose, systolic blood pressure (SBP), and the highest level of high-density lipoprotein (HDL) cholesterol. Medium-risk profile (50.4%), having intermediate indicators of those factors. The high-risk profile (7.0%) was characterized by the lowest HDL cholesterol and the highest values of triglycerides, fasting glucose, SBP, and BMI. Conclusions: Compared to the low-risk profile, medium- and high-risk biological profiles were independently associated with higher odds of CVD, according to sociodemographic and lifestyle factors. The study suggests that integrating multiple biological risk factors rather than a single risk factor in clinical practice may enhance diagnostic accuracy.
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