Knowledge, affirmativeness, and experience as factors associated with self-efficacy in LGBTQ+-affirmative services for individuals with IDD.

Abdominal aortic aneurysm (AAA) remains a clinically important vascular disease because it often progresses silently before rupture, while current management still relies primarily on screening, surveillance, cardiovascular risk reduction, and timely repair rather than on effective disease modifying pharmacological therapies (). In contemporary epidemiological research on AAA, increasing attention has been directed toward more refined risk stratified approaches to case identification. Most existing prediction models regard age, sex, smoking, body size, hypertension, dyslipidemia, and a history of cardiovascular disease as the principal determinants of risk (, ), yet substantial heterogeneity in susceptibility remains unexplained. Moreover, AAA is unlikely to arise from isolated risk factors alone (). Emerging cardiovascular research increasingly supports an exposome perspective, emphasizing that environmental, socioeconomic, psychosocial, and behavioral factors tend to cluster in real life and may jointly contribute to residual vascular risk beyond traditional clinical predictors (, ). In AAA, socioeconomic disadvantage has been associated with more severe clinical presentations, including aneurysm rupture (), while unhealthy lifestyle patterns have also shown joint associations with aneurysm incidence in large population based cohorts (). However, most AAA studies still focus on single exposures or a limited set of conventional predictors and therefore fail to fully capture the cumulative impact of multidomain exposure burden. Metabolomics provides a highly promising intermediate layer linking upstream exposures to downstream aneurysm biology (). Recent biomarker studies have shown that amino acid, inflammatory, and lipid related signatures are associated with AAA size, growth, or progression (). However, most previous biomarker studies have been conducted in relatively small clinical cohorts and have rarely been integrated with multidomain exposure assessment in prospective population based studies. Therefore, it remains unclear whether exposure related metabolomic signatures mediate the association between cumulative exposure burden and incident AAA, and whether such information can improve risk prediction beyond clinical characteristics alone.