Association of erythrocyte fatty acid profiles with the presence and rupture of intracranial aneurysms

Risk stratification for intracranial aneurysm (IA) rupture remains suboptimal. Disrupted lipid metabolism may contribute to IA development and rupture. As stable biomarkers of long-term lipid status, evaluation of erythrocyte fatty acids may aid in risk assessment, although their clinical relevance remains unclear. This case‒control study included 175 participants (64 controls without IAs, 61 with unruptured IAs, and 50 with ruptured IAs) who were recruited in 2024. Twenty-two erythrocyte fatty acids were quantified by gas chromatography, and their associations with IA presence and rupture were assessed using logistic regression. Fatty acids associated with rupture were identified using least absolute shrinkage and selection operator (LASSO) regression and incorporated into established rupture risk models (PHASES, ELAPSS, and UCAS). Incremental discriminatory value was evaluated by comparing the change in the area under the receiver operating characteristic (ROC) curve (ΔAUC) using DeLong’s test. Validation was performed in an independent external cohort comprising 53 participants (27 with unruptured IAs and 26 with ruptured IAs). High erythrocyte levels of total n-3 polyunsaturated fatty acids (PUFAs) were associated with decreased IA risk (OR 0.28, 95% CI 0.17–0.46), whereas high levels of saturated fatty acids, monounsaturated fatty acids, n-6 PUFAs and trans fatty acids were associated with increased IA risk (all P < 0.01). Total n-3 PUFA (OR 0.57, 95% CI 0.36–0.91; P = 0.019) and n-6 PUFA (OR 1.81, 95% CI 1.14–2.87; P = 0.012) levels were also associated with rupture. Five key fatty acids identified by LASSO (C18:1, C20:2n-6, C20:4n-6, C20:5n-3, C22:6n-3) significantly improved rupture discrimination (ΔAUC 0.134–0.163; all P < 0.05). In the validation cohort, fatty acid integration similarly enhanced discrimination across all risk models (all P < 0.05). Erythrocyte fatty acid profiles are associated with IA presence and rupture. Incorporation of these profiles into existing risk models has the potential to refine rupture risk prediction and inform individualized risk stratification, pending validation in prospective or longitudinal cohorts.