The association between neutrophil percentage-to-albumin ratio and post-stroke depression: a cross-sectional study
Year: 2026
Type: article
Study design: Observational Study
Abstract: The neutrophil percentage-to-albumin ratio (NPAR) is a cost-effective and readily accessible novel inflammatory biomarker, but its association with post-stroke depression (PSD) remains underexplored. This study aims to investigate the relationship between NPAR and PSD, providing a foundation for the clinical prevention and treatment of stroke. We used data from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018 in this cross-sectional analysis, applying a multivariate logistic regression model to explore the association between NPAR and PSD, using restricted cubic splines (RCS) to evaluate their non-linear relationship, and exploring heterogeneity through subgroup analysis and interaction tests. Receiver operating characteristic (ROC) curve analysis was used to assess the predictive performance of NPAR for PSD. Additionally, a sensitivity analysis was conducted to assess the robustness of the results. This study included a total of 32,220 participants, and there was a significant positive correlation between NPAR levels and PSD risk. In the fully adjusted model, the positive correlation between NPAR and PSD remains stable (OR = 1.087; 95% CI, 1.024–1.154). RCS analysis indicates a linear association between NPAR and PSD. Subgroup analysis revealed that this association was significantly present in men, age > 60 years, smoking, drinking, hypertension, diabetes, BMI ≥ 25 kg/m 2 , and no strenuous physical activity. Moreover, the ROC curve analysis showed that the area under the curve (AUC) of NPAR for predicting PSD was 0.62 (95% CI 0.58–0.66). This study demonstrated a positive association between NPAR and PSD. However, the cross-sectional design precludes causal inference, and prospective studies are needed to further elucidate the relationship.
Authors: Xinglu Dong, Fei Wang, Mengna Liu
Institution: Jiaxing University
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,190
OpenAlex ID: W7137325858