Combining Biological Aging and Genetic Susceptibility to Identify High-Risk Populations for Venous Thromboembolism

Academic Background

Venous thromboembolism (VTE), including deep vein thrombosis (DVT) and pulmonary embolism (PE), is the third most fatal cardiovascular disease globally. The incidence of VTE is closely related to age, particularly in individuals over 40, where the risk nearly doubles with each additional decade. However, age alone cannot fully reflect the rate of biological aging in individuals. The rate of biological aging refers to the difference between an individual’s biological age and chronological age, which may lead to significant differences in disease risk among individuals of the same age group. Therefore, exploring the role of biological aging rate in the development of VTE is of great significance.

In recent years, researchers have proposed various measures of biological aging, such as DNA methylation data, telomere length (TL), and clinical parameters. Among these, phenotypic age (Phenotypic Age) is a novel aging indicator based on chronological age and nine clinical biomarkers, which has been proven to effectively predict all-cause mortality. Additionally, phenotypic age is associated with the long-term risks of chronic respiratory diseases, depression, and COVID-19. Phenotypic age acceleration (PhenoAgeAccel) is calculated as the residual from a linear regression of phenotypic age against chronological age, reflecting the rate of biological aging.

Source of the Paper

This paper was co-authored by researchers from the First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen Memorial Hospital, and other institutions, including Zhensheng Hu, Jiatang Xu, and Runnan Shen. It was published in American Journal of Hematology, Issue 0, 2025. The study was funded by the National Natural Science Foundation of China (81800420).

Research Process and Results

Research Process

1. Study Population: The study was based on 394,041 participants from the UK Biobank, excluding individuals with a history of VTE, lack of genetic data, or withdrawal of consent.
2. Calculation of Phenotypic Age Acceleration: Phenotypic age was calculated using chronological age and nine plasma biomarkers (e.g., C-reactive protein, glucose, creatinine). PhenoAgeAccel was derived as the residual from a linear regression of phenotypic age against chronological age, reflecting the rate of biological aging.
3. Polygenic Risk Score (PRS): Two PRS models (PRS93 and PRS297) were constructed based on single nucleotide polymorphisms (SNPs) associated with VTE. Participants were categorized into high, medium, and low genetic risk groups based on PRS distribution.
4. Statistical Analysis: Cox proportional hazards models were used to evaluate the relationship between PhenoAgeAccel, genetic risk, and VTE incidence. Restricted cubic splines (RCS) were employed to explore nonlinear relationships. Additionally, mediation analyses were conducted to investigate the mediating role of PhenoAgeAccel in the associations between cancer, obesity, and VTE.

Key Findings

1. PhenoAgeAccel and VTE Risk: The study found a significant association between PhenoAgeAccel and VTE risk (HR 1.37, 95% CI: 1.32–1.42). Individuals with accelerated biological aging (PhenoAgeAccel > 0) had a higher risk of VTE, DVT, and PE compared to those with slower biological aging (PhenoAgeAccel < 0).
2. Genetic Risk and VTE Risk: The high genetic risk group had a significantly higher risk of VTE compared to the low genetic risk group (HR 2.69, 95% CI: 2.54–2.85). The PRS93 model outperformed the PRS297 model in predicting VTE risk (AUC 0.602 vs. 0.588).
3. Combined Effect of PhenoAgeAccel and Genetic Risk: Individuals with accelerated biological aging and high genetic risk had a 3.83-fold higher risk of VTE (95% CI: 3.51–4.18) compared to those with slower biological aging and low genetic risk. PhenoAgeAccel and genetic risk showed significant additive interactions in VTE, DVT, and PE.
4. Mediation Analysis: PhenoAgeAccel mediated approximately 6% of the association between cancer and VTE and about 20% of the association between obesity and VTE.

Conclusions and Significance

This study demonstrates that PhenoAgeAccel is significantly associated with VTE risk and can be combined with genetic risk to improve the accuracy of VTE risk stratification. As an easily accessible clinical biomarker, PhenoAgeAccel holds promise for guiding VTE prevention and treatment strategies. Additionally, the study reveals the mediating role of PhenoAgeAccel in the associations between cancer, obesity, and VTE, providing new insights into the complex relationships among these conditions.

Research Highlights

1. Novel Biomarker: PhenoAgeAccel, as a new biological aging indicator, was applied for the first time to predict VTE risk, showcasing its potential in clinical practice.
2. Combined Effect Analysis: The study is the first to combine PhenoAgeAccel with genetic risk, revealing their synergistic effects on VTE risk and offering new approaches for precision prevention.
3. Mediation Effect Exploration: Through mediation analysis, the study uncovered the mediating role of PhenoAgeAccel in the associations between cancer, obesity, and VTE, providing new evidence for understanding the pathogenesis of these diseases.

Additional Valuable Information

The study also compared the predictive performance of PhenoAgeAccel with that of the traditional aging biomarker, telomere length, and found that PhenoAgeAccel performed better in predicting VTE risk. Furthermore, the robustness of the results was validated through stratified and sensitivity analyses.

This study provides new biomarkers and methods for VTE risk stratification and precision prevention, offering significant scientific and practical value.