Disparities in the Prevalence and Risk Factors for Carotid and Lower Extremities Atherosclerosis in a General Population-Bialystok PLUS Study.
Anna LisowskaMarlena DubatowkaMałgorzata ChlabiczJacek JamiołkowskiMarcin KondraciukAnna SzyszkowskaMałgorzata KnappAnna SzpakowiczAdam ŁukasiewiczKarol Adam KamińskiPublished in: Journal of clinical medicine (2023)
This study was conducted in a representative sample of area residents aged 20-80 years old. The aim of the study was to assess the prevalence of classic risk factors of atherosclerosis in the studied population and to search for new risk factors in these patient subpopulations. A total of 795 people (mean age 48.64 ± 15.24 years, 45.5% male) were included in the study group. Two independent data analyses were performed. In the first analysis, the study group was divided into two subgroups depending on the presence or absence of atherosclerotic plaques in carotid arteries (APCA). APCA were observed in 49.7% of the study group: in the population aged between 41 and 60 years in 49.3%, and those between 61 and 70 years in 86.3%. Patients with APCA were more often diagnosed with arterial hypertension, diabetes, and hypercholesterolemia. In the second analysis, the study group was divided into two subgroups depending on the presence of lower extremities atherosclerotic disease (LEAD). Patients with an ABI (ankle-brachial index) ≤ 0.9 constituted 8.5% of the study group, and they were significantly older, and more often diagnosed with diabetes and APCA. To identify the factors most strongly associated with APCA and an ABI ≤ 0.9, logistic regression was used, with stepwise elimination of variables. The strongest factors associated with APCA were current smoking and diastolic central pressure. We did not note such an association and did not find additional parameters to facilitate the diagnosis of LEAD in asymptomatic patients. The most important observation in our study was the high prevalence of APCA in the study population, especially in the group of young people under the age of 60.
Keyphrases
- risk factors
- blood pressure
- cardiovascular disease
- healthcare
- physical activity
- heart failure
- machine learning
- metabolic syndrome
- end stage renal disease
- chronic kidney disease
- artificial intelligence
- coronary artery disease
- big data
- arterial hypertension
- peritoneal dialysis
- weight loss
- insulin resistance
- left ventricular
- middle aged
- data analysis
- skeletal muscle