Precision and personalized medicine have brought a significant transformation in the management of dyslipidemia. This approach, by considering the unique characteristics of each patient, enables targeted treatments, reduces side effects, and improves quality of life. The development and expansion of these approaches can significantly contribute to reducing the burden of cardiovascular diseases and increasing the lifespan and health of the community. Key tools include omics technologies, such as genomics, proteomics, metabolomics, and pharmacogenomics, which help identify genetic variants, protein profiles, and predict drug responses. Big data analytics and artificial intelligence, including machine learning, enhance risk prediction and treatment optimization by analyzing clinical records and integrating genomic, environmental, and social data. Numerous genetic mutations and variants play a role in dyslipidemia; beyond such well-known genes as LDLR, APOB, and PCSK9, multiple loci identified through large-scale genome-wide association studies are involved. By identifying these mutations through genetic testing, physicians can design more targeted therapies, such as selecting the appropriate drug type and optimal dosage, and even utilizing novel treatments, including PCSK9 inhibitors or RNA-based therapies tailored to each patient. Integrating knowledge of genetic, environmental, and lifestyle factors lays the foundation for precision medicine, enabling the design of treatments perfectly tailored to the unique characteristics of every individual.
Type of Study:
Review |
Subject:
Genetics Received: 2025/06/21 | Accepted: 2025/09/10 | ePublished ahead of print: 2025/09/24