Association between PCSK9 and post-stroke cognitive impairment: a retrospective cohort study and predictive models establishment based on machine learning.
5 affiliations
- 1Department of Neurology, the First Affiliated Hospital (Yijishan Hospital) of Wannan Medical College, Wuhu City, Anhui Province, China.
- 2Department of Neurology, Huaian 82 Hospital, Huaian, Jiangsu, China.
- 3Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Public Health Clinical Center, Hefei, China.
- 4Department of Neurology, Huaian 82 Hospital, Huaian, Jiangsu, China. [email protected].
- 5Department of Neurology, BaoYing Clinical Medical College of YangZhou University, Jiang Su, China. [email protected].
Abstract
Background and purpose
Proprotein convertase subtilisin/kexin type 9 (PCSK9) has gained increasing attention due to its involvement in lipid metabolism and neuroinflammation regulation. High PCSK9 levels are linked to an elevated risk of cerebrovascular events and small vessel diseases. However, systematic research exploring the association between PCSK9 and post-stroke cognitive impairment (PSCI) remains scarce. This study investigated the link between PCSK9 and PSCI.Methods
A cohort of 354 patients with PSCI was enrolled in this investigation. The link between PCSK9 and PSCI occurrence was evaluated through multivariate logistic regression analysis. Restricted cubic spline methodology was employed to examine the non-linear association between PCSK9 levels and PSCI risk. Prediction models were developed using machine learning algorithms.Results
After adjusting for confounders, elevated PCSK9 levels were identified as an independent predictor of PSCI. Including PCSK9 in prediction models alongside traditional risk factors significantly improved the accuracy of PSCI predictions. Feature importance rankings from the SVM model highlighted the significance of PCSK9, with the SVM-based model yielding the highest performance (AUC = 0.895).Conclusion
PCSK9 blood levels in individuals after acute ischemic stroke function as an independent predictor for PSCI and improve the precision of machine learning-based predictive models.
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BibTeX
@article{wang2026association,
title = {{Association between PCSK9 and post-stroke cognitive impairment: a retrospective cohort study and predictive models establishment based on machine learning.}},
author = {Ling Wang and Xin Zhang and Xinyu Fan and Ling Chen and Qiantao Cheng},
journal = {BMC neurology},
year = {2026},
month = aug,
doi = {10.1186/s12883-026-05226-4},
url = {https://europepmc.org/article/MED/42968076},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Association between PCSK9 and post-stroke cognitive impairment: a retrospective cohort study and predictive models establishment based on machine learning.
AU - Ling Wang
AU - Xin Zhang
AU - Xinyu Fan
AU - Ling Chen
AU - Qiantao Cheng
PY - 2026
DA - 2026/08/01
JO - BMC neurology
DO - 10.1186/s12883-026-05226-4
UR - https://europepmc.org/article/MED/42968076
AB - Background and purpose Proprotein convertase subtilisin/kexin type 9 (PCSK9) has gained increasing attention due to its involvement in lipid metabolism and neuroinflammation regulation. High PCSK9 levels are linked to an elevated risk of cerebrovascular events and small vessel diseases. However, systematic research exploring the association between PCSK9 and post-stroke cognitive impairment (PSCI) remains scarce. This study investigated the link between PCSK9 and PSCI.Methods A cohort of 354 patients with PSCI was enrolled in this investigation. The link between PCSK9 and PSCI occurrence was evaluated through multivariate logistic regression analysis. Restricted cubic spline methodology was employed to examine the non-linear association between PCSK9 levels and PSCI risk. Prediction models were developed using machine learning algorithms.Results After adjusting for confounders, elevated PCSK9 levels were identified as an independent predictor of PSCI. Including PCSK9 in prediction models alongside traditional risk factors significantly improved the accuracy of PSCI predictions. Feature importance rankings from the SVM model highlighted the significance of PCSK9, with the SVM-based model yielding the highest performance (AUC = 0.895).Conclusion PCSK9 blood levels in individuals after acute ischemic stroke function as an independent predictor for PSCI and improve the precision of machine learning-based predictive models.
SN - 1471-2377
ER -