Department of Cardiology, Suzhou Municipal Hospital, Gusu School, The Affiliated Suzhou Hospital of Nanjing Medical University, Nanjing Medical University, 215008, Suzhou, China.
Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, 210029, Nanjing, China.
BMC Public Health. 2024 Jan 2;24(1):50. doi: 10.1186/s12889-023-17556-w.
There is an increasing awareness that diet-related inflammation may have an impact on the stroke. Herein, our goal was to decipher the association of dietary inflammatory index (DII) with stroke in the US general population.
We collected the cross-sectional data of 44,019 participants of the National Health and Nutrition Examination Survey (NHANES) 1999-2018. The association of DII with stroke was estimated using weighted multivariate logistic regression, with its nonlinearity being examined by restricted cubic spline (RCS) regression. The least absolute shrinkage and selection operator (LASSO) regression was applied for identifying key stroke-related dietary factors, which was then included in the establishment of a risk prediction nomogram model, with the receiver operating characteristic (ROC) curve being built to evaluate its discriminatory power for stroke.
After confounder adjustment, the adjusted odds ratios (ORs) with 95% confidence intervals (CIs) for stroke across higher DII quartiles were 1.19 (0.94-1.54), 1.46 (1.16-1.84), and 1.87 (1.53-2.29) compared to the lowest quartile, respectively. The RCS curve showed a nonlinear and positive association between DII and stroke. The nomogram model based on key dietary factors identified by LASSO regression displayed a considerable predicative value for stroke, with an area under the curve (AUC) of 79.8% (78.2-80.1%).
Our study determined a nonlinear and positive association between DII and stroke in the US general population. Given the intrinsic limitations of cross-sectional study design, it is necessary to conduct more research to ensure the causality of such association.
人们越来越意识到与饮食相关的炎症可能对中风有影响。在此，我们旨在探讨美国一般人群饮食炎症指数（DII）与中风之间的关联。
我们收集了 1999 年至 2018 年全国健康和营养调查（NHANES）中 44019 名参与者的横断面数据。使用加权多变量逻辑回归估计 DII 与中风的关联，并用限制性立方样条（RCS）回归检验其非线性。应用最小绝对收缩和选择算子（LASSO）回归来识别与中风相关的关键饮食因素，然后将其纳入中风风险预测列线图模型的建立中，构建接收者操作特征（ROC）曲线以评估其对中风的判别能力。
在调整混杂因素后，与 DII 最低四分位相比，DII 较高四分位的中风校正比值比（OR）及其 95%置信区间（CI）分别为 1.19（0.94-1.54）、1.46（1.16-1.84）和 1.87（1.53-2.29）。RCS 曲线显示 DII 与中风之间呈非线性正相关。基于 LASSO 回归识别的关键饮食因素的列线图模型显示出对中风的相当大的预测价值，曲线下面积（AUC）为 79.8%（78.2-80.1%）。
我们的研究确定了美国一般人群中 DII 与中风之间的非线性正相关。鉴于横断面研究设计的固有局限性，有必要进行更多的研究以确保这种关联的因果关系。
