Wu Zhounan, Liang Yuhang, Zeng Li, Fang Jiayi, He Jinshen
Department of Orthopaedic Surgery, the Third Xiangya Hospital of Central South University, 138 Tongzipo Road, Hexi Yuelu District, Changsha City, 410013, Hunan Province, China.
Xiangya School of Medicine, Central South University, Changsha, 410013, China.
J Health Popul Nutr. 2025 Aug 18;44(1):295. doi: 10.1186/s41043-025-01015-w.
Although inflammation and dietary habits have been linked to obesity-associated hepatic steatosis, the role of diet-mediated inflammation in the course of hepatic steatosis remains unclear. This study aims to investigate the association between the Dietary Inflammatory Index (DII) and hepatic steatosis in the US population.
The cross-sectional portion of this study included participants from the National Health and Nutrition Examination Survey (NHANES) spanning from 2007 to 2020. Our investigation employed weighted logistic regression to explore the association between DII score and hepatic steatosis prevalence. Subgroup analyses were performed to identify potential confounding factors, while smooth curve fitting and threshold effect analysis were utilized to determine any non-linear relationships. Least absolute shrinkage and selection operator (LASSO) regression was used to identify the key nutrients associated with the risk of hepatic steatosis. Subsequently, a nomogram model based on these major dietary factors was constructed. The diagnostic power of the predictive model to screen for hepatic steatosis risk was evaluated using receiver operating characteristic (ROC) curves and calibration curves.
This study included a substantial cohort of 27,655 participants, representing 181 million residents in the U.S. Weighted logistic regression revealed a positive association between elevated DII scores and the risk of hepatic steatosis (DII as a continuous variable: OR = 1.07, 95% CI = 1.05-1.10; Q4 compared to Q1: OR = 1.35, 95% CI = 1.21-1.50). The nomogram model exhibited considerable power in disease risk assessment (the area under the curve (AUC) of the training set: 0.714, 95% CI = 0.706-0.721; the AUC of the validation set: 0.707, 95% CI = 0.697-0.716). The results of the sensitivity analyses indicated that a history of current or past hepatitis B infections and excessive alcohol did not interfere with the association between DII and hepatic steatosis.
Our findings reveal a significant association between the DII score and hepatic steatosis. This suggests that dietary patterns with lower inflammatory potential may be associated with a reduced prevalence of hepatic steatosis. However, given the inherent limitations of the cross-sectional study design, further longitudinal studies or intervention trials are necessary to ensure such an association.
尽管炎症和饮食习惯与肥胖相关的肝脂肪变性有关，但饮食介导的炎症在肝脂肪变性过程中的作用仍不清楚。本研究旨在调查美国人群中饮食炎症指数（DII）与肝脂肪变性之间的关联。
本研究的横断面部分纳入了2007年至2020年美国国家健康和营养检查调查（NHANES）的参与者。我们采用加权逻辑回归来探讨DII评分与肝脂肪变性患病率之间的关联。进行亚组分析以识别潜在的混杂因素，同时利用平滑曲线拟合和阈值效应分析来确定是否存在非线性关系。使用最小绝对收缩和选择算子（LASSO）回归来识别与肝脂肪变性风险相关的关键营养素。随后，基于这些主要饮食因素构建了列线图模型。使用受试者工作特征（ROC）曲线和校准曲线评估预测模型筛查肝脂肪变性风险的诊断能力。
本研究纳入了27655名参与者的大量队列，代表了美国1.81亿居民。加权逻辑回归显示，DII评分升高与肝脂肪变性风险呈正相关（DII作为连续变量：OR = 1.07，95%CI = 1.05 - 1.10；与Q1相比，Q4：OR = 1.35，95%CI = 1.21 - 1.50）。列线图模型在疾病风险评估中表现出相当大的能力（训练集曲线下面积（AUC）：0.714，95%CI = 0.706 - 0.721；验证集AUC：0.707，95%CI = 0.697 - 0.716）。敏感性分析结果表明，当前或既往乙型肝炎感染史和过量饮酒并不干扰DII与肝脂肪变性之间的关联。
我们的研究结果揭示了DII评分与肝脂肪变性之间存在显著关联。这表明炎症潜力较低的饮食模式可能与肝脂肪变性患病率降低有关。然而，鉴于横断面研究设计的固有局限性，需要进一步的纵向研究或干预试验来证实这种关联。
