Leukocyte Telomere Length and Its Polygenic Risk Score in Post-Stroke Cognitive Impairment: Evidence From a Multicenter Cohort Study

BACKGROUND: Post-stroke cognitive impairment (PSCI) is a frequent and disabling consequence of ischemic stroke. Leukocyte telomere length (LTL), a biomarker of systemic biological aging, has been implicated in cognitive outcomes, but evidence in stroke populations remains limited. We investigated whether baseline LTL and an LTL polygenic risk score (LTL-PRS) are associated with PSCI risk in a large Chinese stroke cohort. METHODS: We analyzed patients with ischemic stroke or transient ischemic attack (TIA) from the Third China National Stroke Registry-impairment of cognition and sleep (CNSR-III-ICONS) sub study. Baseline LTL was estimated from whole-genome sequencing (WGS) using TelSeq. An 18-SNP LTL-PRS (East-Asian weights) was z-standardized. Cognitive status at 12 months was assessed using the Montreal Cognitive Assessment (MoCA); PSCI was defined as an age-, sex-, education-adjusted norm-referenced threshold (z score ≤ -1.5). Fifty-one participants missing education required for the adjusted PSCI definition were excluded, leaving 923 for analysis. Multivariable logistic regression tested associations of LTL and LTL-PRS with PSCI, with exploratory interaction and stratified analyses by age, sex, and stroke subtype. RESULTS: PSCI occurred in 191 of 923 patients (20.7%). Neither continuous LTL nor LTL-PRS was associated with PSCI overall. Exploratory analyses provided nominal evidence of heterogeneity by age (p-interaction = 0.038) and stroke subtype (p-interaction = 0.01), whereas evidence for sex interaction was inconclusive (p-interaction = 0.07). Associations with the MoCA executive function domain were weak and sensitive to covariate adjustment. CONCLUSIONS: Baseline telomere metrics showed, at most, modest and context-dependent associations with PSCI. Neither LTL nor this limited-variant LTL-PRS demonstrated overall prognostic utility, highlighting the need for integrated, longitudinal multi-omic approaches for individualized cognitive risk prediction after stroke.