Mathematical modeling of the dependence of the risk of vitamin D deficiency on anthropometric and laboratory parameters

• Main Authors: Anton Shanyhin (Author), Volodymyr Babienko (Author), Yevhen Strakhov (Author), Arina Korkhova (Author)
• Format: Book
• Published: Kazimierz Wielki University, 2023-04-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.12775/JEHS.2023.13.04.042; 2391-8306
• Keywords: mathematical modeling; vitamin D; anthropometry; blood lipids; prevention; Education; L; Sports; GV557-1198.995; Medicine; R
• Source: Journal of Education, Health and Sport, Vol 13, Iss 4 (2023)

Recently, data on the use of mathematical models to determine the status of vitamin D in clinical practice in patients with various diseases appear in the literature, but there is no data on the creation of a mathematical model that allows screening in the general population. The creation of a mathematical model, with the help of which it is possible to identify groups at risk of vitamin D deficiency, will contribute to the reduction of laboratory research costs and allow screening of vitamin D status on a more massive and reasonable basis. Goal. Study of the relationship between the risk of vitamin D deficiency and anthropometric and laboratory parameters using mathematical modeling methods. Materials and methods. 928 residents of the southern region of Ukraine between the ages of 19 and 82 (average age - 47.2 years) were surveyed. Based on the correlation analysis of the studied data, the most influential data were selected as the following indicators: age, body mass index, atherogenicity coefficient, high-density lipoproteins, and the ratio of waist volume to hip volume. Conclusions. The study emphasizes the importance of anthropometric and laboratory parameters in predicting the risk of vitamin D deficiency. The use of mathematical modeling methods makes it possible to identify the most important risk factors and quantify their contribution. The results of the study may have important implications for public health by helping to identify high-risk groups in a timely manner.