Thesis supervisor: Vince Fazekas-Pongor
Location of studies (in Hungarian): Semmelweis Egyetem, Népegészségtani Intézet Abbreviation of location of studies: SENI
Description of the research topic:
Accurate risk prediction in medicine is of utmost importance. By identifying individuals with elevated risk and tailoring interventions to their risk status, we can prevent the development of diseases and slow down their progression. These models can also help the clinician to motivate patients and increase their compliance. Risk prediction, however, is also important from a public health perspective to assess disease burden and help determine priorities.
The Hungarian society faces several challenges caused by the deteriorating demographic trends and by the changes in certain lifestyle and other factors affecting the general health of the population. According to the most recent data, Hungarian life expectancy at birth is five years shorter than that of the European Union average. The leading cause of death in Hungary are cardiovascular diseases, accounting for approximately 50% of all-cause mortality. Among cardiovascular diseases, stroke and ischemic heart diseases alone account for nearly 30% of deaths. The second most important cause of death is related to cancers, which are responsible for 25% of all deaths. An estimated 40% of the population may be affected by one or more chronic conditions, such as diabetes mellitus, hypertension, overweight, obesity, pulmonary or musculoskeletal diseases. The occurrence of these diseases increases in the presence of certain lifestyle factors, such as smoking, unhealthy diet, excessive alcohol consumption, insufficient physical activity, or pollution. According to certain studies, these aforementioned factors contribute in a much larger extent to disease burden in Hungary as compared to other European countries.
In the literature, several risk prediction models exist. For the risk assessment of cardiovascular diseases, the most frequently used models are the Framingham Risk Score, SCORE2, SCORE2-OP, QRisk and updated versions, Globorisk, and WHO CVD Risk Chart. The most important predictors for diabetes mellitus are age, sex, family history, body mass index, hypertension, increased waist circumference, ethnicity, smoking, and unhealthy diets. To predict the development of hypertension, the Framingham Hypertension Risk Equation and the Hopkins Hypertension Score are often used. To measure the burden of obesity in the next 10 years, the OPoRT model can be used. These models, however, have not been tailored to the Hungarian population, which can lead to biased risk prediction.
The main goal of the topic is to define the role and strength of predictors for the occurrence of the following diseases:
• Cardio- and cerebrovascular diseases
• Hypertension
• Diabetes mellitus type 2
• Overweight and obesity
• Cancers
• Mental diseases
• Impaired cognition
• Musculoskeletal disease
• Pulmonary diseases
Our secondary aims are to create new risk prediction models and to tune the already available risk prediction models for the Hungarian population. This will allow for a more precise prediction that can help not only the work of the clinician but can also contribute to better planning of public health interventions by helping define priorities and populations at higher risk.
Deadline for application: 2024-05-15
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
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