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Inflammatory-neurological deficit versus metabolic dysregulation: unsupervised clustering and SHAP analysis
1Department of Postgraduate, School of Clinical Medicine, Beihua University, Jilin, China.
Objective:
This retrospective cohort study enrolled 20,538 middle-aged and older patients with acute ischemic stroke (AIS) to identify immune-metabolic clinical subtypes by unsupervised clustering, to examine the differential association between subtype characteristics and post-stroke epilepsy (PSE) susceptibility, and to clarify immune-related threshold biomarkers for individualized PSE risk stratification.
Methods:
Seven standardized variables were used for K-means clustering: age; National Institutes of Health Stroke Scale (NIHSS) score; lipid profile components (triglycerides, low-density lipoprotein cholesterol [LDL-c], high-density lipoprotein cholesterol [HDL-c]); glycated hemoglobin (HbA1c); and the immune-inflammatory marker C-reactive protein (CRP). Two immune-metabolic subtypes were determined. Multivariable logistic regression was performed to quantify the association between Cluster and PSE. An Extra Trees-based SHapley Additive exPlanations (SHAP) framework was used to interpret the hierarchical contribution of immune and metabolic indicators to PSE risk within each subtype.
Results:
Clustering produced two clinically distinct subtypes. Cluster 1, the inflammatory-neurological deficit cluster (n = 7,790), had older age, higher NIHSS scores, higher CRP, and higher HDL-c. Cluster 2, the metabolic dysregulation cluster (n = 12,748), had higher HbA1c, LDL-c, and triglycerides (TG). The incidence of PSE was substantially higher in the inflammatory-neurological deficit cluster than in the metabolic dysregulation cluster (7.2% vs. 2.4%, P < 0.001). After full adjustment, membership in the inflammatory-neurological deficit cluster was associated with 4.31-fold higher odds of PSE. Extra Trees-based SHAP analysis identified distinct drivers in each cluster. In the inflammatory-neurological deficit cluster, CRP emerged as the leading predictor with a risk-acceleration threshold above 24 mg/L. In the metabolic dysregulation cluster, NIHSS was the top predictor, and HbA1c values below 7% were associated with a protective effect.
Conclusion:
Unsupervised clustering separated AIS patients into two subtypes. The inflammatory-neurological deficit cluster had higher PSE risk. Cluster-specific SHAP results suggested candidate thresholds for individualized risk assessment.
