Feng Qinghui, Miao Chanchan, Gao Xuejun
Yan'an Medical College of Yan'an University, Yan'an, 716000, China.
Department of Neurology, Yan'an University Affiliated Hospital, Yan'an, 716000, China.
J Stroke Cerebrovasc Dis. 2025 Aug;34(8):108353. doi: 10.1016/j.jstrokecerebrovasdis.2025.108353. Epub 2025 May 20.
The high-sensitivity C-reactive protein (Hs-CRP)-to-high-density lipoprotein cholesterol (HDL-C) ratio, which integrates insights into inflammation and lipid metabolism, serves as a comprehensive indicator. The association between this ratio and stroke prevalence is endeavored to be explored in this research.
Drawing on information gathered during the 2015-2018 cycles of the NHANES, the association between the Hs-CRP/HDL-C ratio and stroke was examined through multivariate logistic regression. Additionally, subgroup analysis, interaction test, and restricted cubic spline (RCS) were carried out. Multiple machine learning methods were used to identify the key factors affecting stroke and combined with Shap interpretable models to determine the degree of influence of the key factors. Finally, the results of the logistic regression analysis are used to construct a predictive model, which is represented using a nomogram.
This research sample comprised 8,064 participants, yielding a stroke prevalence of 4.04%. A positive correlation was shown between the Hs-CRP/HDL-C ratio and stroke (OR: 1.17, 95% CI: 1.02, 1.35). Interaction tests demonstrated that younger participants were more sensitive to higher Hs-CRP/HDL-C ratios, with a significant interaction in stroke. The RCS analysis indicated a nonlinear association between the exposure variable and to outcome variable. The AUC > 0.8 for a random forest model and an XGBoost model demonstrated their strong predictive value. Ultimately, the generated predictive model is a visual nomogram with an AUC of 0.799.
The results of the study showed a positive correlation between Hs-CRP/HDL and the prevalence of stroke, with higher Hs-CRP/HDL levels associated with a higher likelihood of stroke. As a stroke prediction model incorporating Hs-CRP/HDL, the nomogram may play a significant role in the early identification of high-risk populations.
高敏C反应蛋白(Hs-CRP)与高密度脂蛋白胆固醇(HDL-C)的比值整合了炎症和脂质代谢方面的信息,是一项综合指标。本研究旨在探讨该比值与中风患病率之间的关联。
利用2015 - 2018年美国国家健康与营养检查调查(NHANES)收集的信息,通过多因素逻辑回归分析Hs-CRP/HDL-C比值与中风之间的关联。此外,还进行了亚组分析、交互作用检验和限制立方样条(RCS)分析。采用多种机器学习方法识别影响中风的关键因素,并结合Shap可解释模型确定关键因素的影响程度。最后,利用逻辑回归分析结果构建预测模型,并以列线图表示。
本研究样本包括8064名参与者,中风患病率为4.04%。Hs-CRP/HDL-C比值与中风呈正相关(OR:1.17,95%CI:1.02,1.35)。交互作用检验表明,年轻参与者对较高的Hs-CRP/HDL-C比值更为敏感,在中风方面存在显著交互作用。RCS分析表明暴露变量与结局变量之间存在非线性关联。随机森林模型和XGBoost模型的AUC>0.8,显示出较强的预测价值。最终生成的预测模型是一个AUC为0.799的可视化列线图。
研究结果表明Hs-CRP/HDL与中风患病率呈正相关,Hs-CRP/HDL水平越高,中风风险越高。作为纳入Hs-CRP/HDL的中风预测模型,列线图可能在高危人群的早期识别中发挥重要作用。