The role of phenotypic age acceleration in aneurysmal subarachnoid hemorrhage: analysis of retrospective data from two hospital-based cohorts and prospective UK Biobank cohort.

Abstract

Background: Phenotypic age acceleration (PhenoAgeAccel) is considered a major risk factor for various age-related diseases, but its specific role in aneurysmal subarachnoid hemorrhage (aSAH) remains unclear. This study aims to examine the relationship between PhenoAgeAccel and the risk of aSAH, and further explores whether genetic susceptibility modifies this association.

Methods: Using data from the UK Biobank, we performed cross-sectional and prospective analyses to investigate the association between PhenoAgeAccel and aSAH. Polygenic risk scores were calculated to evaluate genetic susceptibility, and interactions between genetic risk and PhenoAgeAccel were explored. Additionally, using hospital cohort data, we applied an XGBoost model interpreted via SHapley Additive exPlanations (SHAP) analysis to identify key clinical predictors, including PhenoAgeAccel, which were subsequently incorporated into a nomogram for clinical risk prediction.

Results: Cross-sectional analyses revealed that each 1-year increment in PhenoAgeAccel was associated with a 1%-7% elevated risk of aSAH. In the UK Biobank, biologically older individuals had a higher risk of aSAH compared to biologically younger individuals (odds ratio [OR] = 1.48; 95% confidence interval [CI], 1.10-1.97; P = 0.009) for PhenoAgeAccel. Similarly, in the hospital datasets, biologically older individuals also showed increased odds of aSAH (Second Xiangya Hospital: OR = 16.45; 95% CI, 4.72-57.34; Fujian Hospital: OR = 12.41; 95% CI, 3.33-46.26). In the prospective analyses of the UK Biobank, PhenoAgeAccel was associated with an increased risk of incident aSAH (hazard ratio [HR] = 1.04; 95% CI, 1.02-1.07). Moreover, additive interactions between PhenoAgeAccel and genetic susceptibility were observed. Further validation using the XGBoost machine learning model and SHAP analysis confirmed PhenoAgeAccel as a key predictive factor for aSAH. Based on these findings, a nomogram integrating PhenoAgeAccel and relevant clinical parameters was developed to facilitate individualized risk prediction in clinical practice.

Conclusion: PhenoAgeAccel is a significant predictor of aSAH risk, particularly among genetically susceptible populations. Identifying individuals with PhenoAgeAccel could serve as a novel clinical biomarker for assessing aSAH.