Estonian research sheds light on how genetic factors influence medication dosing

A research study by the Institute of Genomics at the University of Tartu reveals that genetic variants influencing drug metabolism and disease susceptibility affect medication dosing.

It is well-established that genetic differences contribute to variation in the doses of medication individuals require. In a paper published in the Journal of Translational Medicine, data from 212,000 Estonian Biobank participants were analysed to investigate whether drug prescription data can be used to infer medication dose requirements and link them to genetic variation.

The study focused on commonly used medications for heart disease (statins, metoprolol, warfarin) as well as antidepressants and antipsychotics. As a result, a new data layer was created in the Estonian Biobank, enabling a more detailed view of medication use and providing a valuable resource for a wide range of research questions.

According to the researchers at the pharmacogenomics research group, Silva Kasela and Maris Alver, the derived doses reflect not only genetic influences on drug response but also clinical decision-making and health-related behaviours. “Our method was highly effective at capturing known and biologically meaningful associations. For instance, individuals with certain genetic variants required significantly higher drug doses,” Alver explained, highlighting the method's accuracy in deriving medication doses.

Alver noted that while classical pharmacogenetics often focuses on single high-impact variants, this study aimed to extend the analysis to the genome-wide level and also evaluate associations with predispositions to various complex traits. This is the first study of its kind to systematically infer medication doses from real-world prescription data linked to a biobank and examine their genetic determinants. The analysis was made possible by the comprehensive and longitudinal nature of the Estonian Biobank data.

According to the authors, one of the key findings is that individuals with a higher genetic risk for disease tend to require higher drug doses. For example, people with a higher risk for coronary heart disease required, on average, higher doses of statins (drugs used to lower high cholesterol levels and reduce the risk of cardiovascular disease).

The study also found that the need for higher medication doses is not solely explained by genetic predisposition to disease, but also by the genetics of associated risk factors. For instance, genetic predisposition to weight gain was linked to higher doses of several medications. Similarly, higher levels of HDL cholesterol (“good cholesterol”) were associated with lower doses of metoprolol (a blood pressure medication). The results reflect both disease-driven treatment needs and clinical prescribing decisions. A higher dose should not be feared - it is prescribed when necessary, whether because of elevated disease risk or rapid drug metabolism.

Medication doses also reflect health behaviour

The study also showed that drug doses were associated with genetic traits that reflect health behaviour and socioeconomic background. For instance, genetic variants associated with higher educational attainment were linked to lower statin doses and higher antidepressant doses. The researchers interpret this not as a direct reflection of education level, but rather as an indicator of broader behavioural factors. Individuals with this genetic profile may monitor their health more proactively, regularly attending checkups, actively seeking professional help, and making healthier lifestyle choices. They are also more likely to follow prescribed treatment plans.

A step toward personalised medicine

The new approach developed in this study provides a deeper understanding of how both genetic and non-genetic factors influence optimal treatment. It also supports a more accurate interpretation of what health data can tell us about individual treatment needs. Such analyses are an important step toward advancing the research needed for personalised medicine.

Read more: https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-025-06782-y