Researchers have demonstrated that a polygenic risk score can enhance the prediction and risk stratification of atrial fibrillation in Chinese patients with type 2 diabetes. The study, published in Genome Medicine, analyzed data from two prospective cohorts comprising 20,009 individuals. By integrating genetic data with established clinical metrics, the team showed that a novel risk score provides incremental value beyond the validated CHARGE-AF score. This finding suggests that genetic susceptibility plays a measurable role in identifying high-risk individuals within the diabetic population, where atrial fibrillation is a common adverse outcome.

The research addresses a specific gap in cardiovascular risk assessment for patients with type 2 diabetes. While it is well established that atrial fibrillation is a frequent complication in this group, the role of genetics in identifying those at highest risk has not been comprehensively studied. The study team, led by researchers including Claudia H. T. Tam and Ronald C. W. Ma, sought to determine whether a polygenic risk score could serve as an independent risk factor. They aimed to assess if adding genetic information to standard clinical variables would improve the ability to predict incident atrial fibrillation and other cardiovascular endpoints.

The study utilized a polygenic risk score, designated as PRSAF, which comprises 97 genetic variants previously associated with atrial fibrillation risk. The analysis included 20,009 individuals from two prospective cohorts of Chinese patients with type 2 diabetes. The primary endpoint was incident atrial fibrillation, with secondary endpoints including stroke and congestive heart failure. The researchers calculated the PRSAF for each participant and assessed its associations with these clinical outcomes using Cox regression models. These models were adjusted for baseline covariates to ensure that the genetic signal was independent of other known risk factors.

During the follow-up period, incident atrial fibrillation occurred in 986 patients, representing 5.1% of the total cohort. The results indicated that a higher PRSAF was significantly associated with an increased risk of developing atrial fibrillation. After adjusting for sex, age, duration of diabetes, and principal components, each standard deviation increase in the PRSAF was associated with a hazard ratio of 1.42 for incident atrial fibrillation. The 95% confidence interval for this association was 1.33 to 1.51, indicating a robust statistical relationship between the genetic score and the clinical outcome.

The association between the polygenic risk score and cardiovascular events extended beyond atrial fibrillation. The study found that a higher PRSAF was also associated with an increased risk of stroke, with a hazard ratio of 1.07 per standard deviation increase. Similarly, the risk of congestive heart failure was elevated, with a hazard ratio of 1.10 per standard deviation. These associations remained significant in the fully adjusted model, suggesting that the genetic risk captured by the PRSAF is relevant to a broader range of cardiovascular complications in patients with type 2 diabetes.

To evaluate the practical utility of the genetic score, the researchers compared the predictive performance of the PRSAF against the CHARGE-AF score, a validated clinical risk score. They developed a novel risk score that integrates the PRSAF into the CHARGE-AF framework. This combined score was tested for its ability to improve discrimination and reclassification compared to the CHARGE-AF score alone. The results showed that the integrated score significantly improved both the C-index and the net reclassification improvement index. This indicates that adding genetic information helps to more accurately distinguish between patients who will and will not develop atrial fibrillation.

The study also examined risk stratification by comparing patients in the top quintile of the PRSAF distribution with those in the bottom quintile. Patients in the top quintile were found to be over 2.5-fold more likely to develop atrial fibrillation than those in the bottom quintile. The 95% confidence interval for this comparison was 2.08 to 3.17.

The findings suggest that the polygenic risk score is an independent risk factor for atrial fibrillation in patients with type 2 diabetes. The study provides evidence that genetic factors contribute to the heterogeneity of cardiovascular risk in this population. The study was conducted by a large consortium, including the Hong Kong Diabetes Biobank Study Group and the TRANSCEND Consortium, reflecting a collaborative effort to advance understanding of genetic influences on diabetes complications.

While the study provides strong evidence for the utility of the polygenic risk score, it is important to note the specific population studied. The cohorts consisted of Chinese patients with type 2 diabetes, which may limit the generalizability of the findings to other ethnic groups. The study design was observational, and while the associations were robust after adjustment for covariates, they do not establish causation.

Subject of Research: Polygenic risk score for atrial fibrillation in type 2 diabetes

Article Title: Enhanced prediction and risk stratification of atrial fibrillation with polygenic risk score in Chinese patients with type 2 diabetes

Article References: Tam, C. H. T., Lim, C. K. P., Luk, A. O. Y., Lau, E. S. H., Xie, P. F., Shi, M., Ng, A. C. W., Ozaki, R., Chow, E. W. K., Kong, A. P. S., Chow, E. Y. K., Lee, K. F., Siu, S. C., Hui, G., Tsang, C. C., Lau, K. P., Wong, I. M., Leung, J. Y. Y., Cheung, E. Y. N., … The TRANSCEND Consortium (2026). Enhanced prediction and risk stratification of atrial fibrillation with polygenic risk score in Chinese patients with type 2 diabetes. Genome Medicine. https://doi.org/10.1186/s13073-026-01831-7

Image Credits: AI Generated

DOI: 10.1186/s13073-026-01831-7

Keywords: atrial fibrillation, type 2 diabetes, polygenic risk score, cardiovascular risk, genetics, risk stratification, CHARGE-AF score, Chinese patients, Enhanced, prediction, risk, stratification