Differential Risk Profiles of Incident Abdominal Aortic Aneurysms in Specific Subgroups: A Large Cohort-based Study

Yudiyang Ma; Jianing Wang; Linxi Tang; Feipeng Cui; Yaohua Tian; Jing Zhang; Jian Yang

doi:10.1097/SLA.0000000000006637

Differential Risk Profiles of Incident Abdominal Aortic Aneurysms in Specific Subgroups: A Large Cohort-based Study

Ann Surg. 2025 Jan 23. doi: 10.1097/SLA.0000000000006637. Online ahead of print.

Authors

Yudiyang Ma^{1

2}, Jianing Wang², Linxi Tang², Feipeng Cui², Yaohua Tian^{1

2}, Jing Zhang^{1

3

4}, Jian Yang^{1

3

4}

Affiliations

¹ Hubei Key Laboratory of Ischemic Cardiovascular Disease, Yichang, China.
² Ministry of Education Key Laboratory of Environment and Health, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No.13 Hangkong Road, Wuhan, China, 430030.
³ Department of Cardiology, The First College of Clinical Medical Science, China Three Gorges University & Yichang Central People's Hospital, Yichang, China.
⁴ Hubei Provincial Clinical Research Center for Ischemic Cardiovascular Disease, Yichang, China.

PMID: 39846142
DOI: 10.1097/SLA.0000000000006637

Abstract

Objective: The aim of this study is to explore the risk profiles associated with Abdominal aortic aneurysm (AAA) incidence in both the general population and diverse subpopulations.

Summary background data: AAA is a life-threatening arterial disease, and there is limited understanding of its etiological spectrum across the age, sex, and genetic risk subgroups, making early prevention efforts more complicated.

Methods: This study encompassed a sample size of 364399 participants from the UK. Utilizing the Cox proportional hazards model, we estimated the hazard ratios (HRs) and population attributable fraction (PAF) for 24 risk factors and 5 weighted risk scores associated with AAA incidence. Subsequently, this study investigated the relationships between AAA onset and various risk factors based on age, sex, and genetic susceptibility subgroups, and assessed the two- and three-way interactions.

Results: After a median follow-up of 12.62 years, 1684 participants developed AAA. Among the 24 risk factors from 5 different aspects, 12 exhibited significant associations with AAA development. Socio-demographic factors (age and sex) and genetic factors accounted for the majority of AAA cases in both the general population and diverse subpopulations. For lifestyle factors, AAA cases attributable to smoking are larger in the older group (PAF: 15.45% vs. 11.25%) and women (PAF: 23.79% vs. 16.75%). Similarly, physical inactivity had a greater effect on AAA risk in women (4.84% vs. 1.95%), but no age and genetic risk differences were observed. PAF of high C-reactive protein was the most prominent of all cardiometabolic factors across different age, sex, and genetic risk strata, with 18.92% (< 60 years) and 13.71% (≥ 60 years) in age groups, 18.18% (women) and 13.31% (men) in sex groups, and 17.64% (intermediate genetic risk) and 13.01% (high genetic risk) in genetic risk groups. Clinical comorbidities, such as cardiovascular diseases, dyslipidemia, and hypertension significantly associated with the risk of incident AAA, and these factors exerted a greater influence on AAA risk in younger group, women, and those with low genetic risk (P for interaction < 0.05).

Conclusions: This study depicted specific risk profiles that influence AAA incidence among general population and diverse subpopulations, thereby aiding in the formulation of precise and effective strategies for AAA prevention.