# Association of UGT1A9 Polymorphisms with Cardiac Injury Biomarkers and Clinical Features

## TL;DR

This study finds that UGT1A9 gene variations affect CK-MB levels in cardiac patients, possibly influencing how we interpret heart injury markers.

## Contribution

The study links UGT1A9*3 polymorphisms to CK-MB variability, suggesting implications for personalized cardiac care.

## Key findings

- UGT1A9*3 carriers had higher CK-MB levels compared to other genotypes.
- Troponin I levels were not influenced by UGT1A9 genotypes.
- UGT1A9*3 may indicate statin-related muscle injury rather than true heart damage.

## Abstract

Background/Objectives: This study evaluates the relationship between UGT1A9 polymorphisms, cardiac biomarker patterns, and clinical presentations in patients admitted to the Pamukkale University Emergency Department with cardiac symptoms. Methods: A total of 207 consecutive patients presenting with chest pain, dyspnea, palpitations, or other cardiac complaints were initially enrolled. Patients with incomplete clinical data or unsuccessful genotyping were excluded prior to analysis, and all remaining samples were included in the final evaluation. UGT1A9 *1, *2, and *3 alleles were genotyped using allele-specific PCR and TaqMan® assays. Patients were classified into wt/wt, wt/*3, and *3/*3 groups. Statistical analyses included Kruskal–Wallis, Mann–Whitney U, and chi-square tests. Results: Genotype distribution was 64% wt/wt, 32% wt/*3, and 4% *3/*3. CK-MB levels differed significantly across genotypes (p = 0.006), with the highest in wt/*3 carriers. Troponin I levels showed no genotypic differences (p = 0.533). UGT1A9*3 carriers exhibited elevated CK-MB with relatively low Troponin I, suggesting possible statin-associated muscle injury rather than true myocardial necrosis. Conclusions: UGT1A9 polymorphisms, particularly UGT1A9*3, influence CK-MB variability and may confound the assessment of myocardial injury. Troponin I remains unaffected by genotype. Incorporating UGT1A9 pharmacogenetic testing may contribute to a better understanding of biomarker variability and support future research toward personalized therapeutic strategies.