Association of allostatic load index with cognitive impairment in high-risk stroke populations

Chronic psychosocial stress induces cumulative physiological dysregulation that accelerates biological aging and contributes to the development of cerebrovascular and neurocognitive disorders, including stroke and cognitive impairment. The Allostatic Load Index (ALI) is a composite measure reflecting multisystem physiological wear and tear resulting from chronic stress and has been associated with stroke mortality and cognitive decline. However, evidence regarding the relationship between ALI and cognitive impairment in populations at high risk for stroke remains limited. This study aimed to investigate the association between ALI and cognitive impairment in a stroke high-risk population, and to evaluate the utility of ALI in stroke-related cognition risk stratification. This single-center retrospective observational cohort study was conducted between October 2023 to October 2024. A total of 200 stroke high-risk individuals aged 60 to 85 years, each meeting at least three established stroke risk factors, were included. Thirty-two clinical and laboratory variables were analyzed. Missing data were imputed using the k-nearest neighbors (KNN) algorithm, and multicollinearity was assessed using variance inflation factors (VIF). ALI was calculated based on 10 physiological indicators spanning cardiovascular, metabolic, and inflammatory systems: waist-to-height ratio, systolic blood pressure, diastolic blood pressure, heart rate, neutrophil count, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, glycated hemoglobin, and serum creatinine. One point was assigned for each indicator exceeding clinically validated thresholds (total score range: 0–10). Logistic regression was used to assess the association between ALI and cognitive impairment. Sensitivity analyses categorized participants into quartiles of ALI. Feature selection was performed using the Boruta algorithm. Predictive models were developed using logistic regression, decision trees, random forests, and other machine learning algorithms, with model performance evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Cognitive impairment was identified in 101 participants (50.5%). Individuals with cognitive impairment were older and exhibited significantly higher waist-to-height ratios, systolic blood pressure, and white blood cell counts. Restricted cubic splines analysis identified a threshold waist-to-height ratio of 0.55, above which the risk of cognitive impairment increased markedly. ALI followed a normal distribution, and was positively associated with cognitive impairment (OR = 1.27). The association remained robust after multivariable adjustment (Model II: OR = 1.33). The Boruta algorithm confirmed ALI as an important predictive feature. Among all models, random forest model demonstrated the best discrimination, achieving an AUC of 0.8247. ALI may provide a quantitative measure of cumulative physiological stress burden and appears to be independently associated with cognitive impairment in populations at high risk for stroke. The waist-to-height ratio was identified as a particularly informative component of ALI. These findings suggest the potential utility of integrating ALI into risk assessment frameworks for early identification and prevention of stroke-related cognitive impairment.