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2017 - 2020年美国国家健康与营养检查调查(NHANES)中膳食营养素摄入量与肝脂肪变性和肝纤维化的关联

The association of dietary nutrients consumption with hepatic steatosis and fibrosis from NHANES 2017-2020.

作者信息

Sheng Qi, Liu Shousheng, Yu Haiyang, Shi Huanchen, Xin Yongning

机构信息

Department of Infectious Disease, Qingdao Municipal Hospital, Shandong University, Jinan, China.

Department of Clinical Nutrition, The Affiliated Hospital of Qingdao University, Qingdao, China.

出版信息

Front Nutr. 2025 Jun 24;12:1510860. doi: 10.3389/fnut.2025.1510860. eCollection 2025.

DOI:10.3389/fnut.2025.1510860
PMID:40630174
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12234298/
Abstract

BACKGROUND/OBJECTIVES: Hepatic steatosis and fibrosis represent significant and growing global health burdens. There is an urgent need to seek strategies for early prevention and control of hepatic steatosis and fibrosis. This study attempted to comprehensively evaluate the relationship between dietary nutrient intake and the risk of hepatic steatosis and fibrosis to provide assistance for doctors in guiding the diet of the patients.

METHODS

This observational study assembled 15,560 participants from the 2017-March 2020 cohorts of the National Health and Nutrition Examination Survey. 34 nutrient intake items were included. The liver ultrasound transient elastography was used to evaluate hepatic steatosis and hepatic fibrosis. Various variables, encompassing sociodemographic characteristics, and other potential confounders were considered to ensure the stability of the findings. Additionally, the analysis accounted for various covariates and employed restricted cubic spline analysis to examine potential nonlinear relationships. Weighted quantile sum (WQS) (mixed effect) models were used in the analysis.

RESULTS

The negative correlations were found between low carbohydrate, vitamin C, pyridoxine, magnesium, iron and potassium intake with controlled attenuation parameter (CAP) after adjusting all the covariates and excluding non-linear correlations. Nonlinear correlation was found to exist between the consumption of energy, vitamin E, folate, sodium, alcohol, -Linolenic acid and fish oil and hepatic steatosis ( < 0.05). The negative correlations were showed between low dietary fiber per energy and phosphorous intake with liver stiffness measurement (LSM) after adjusting all the covariates and excluding non-linear correlations ( < 0.05). High caffeine intake showed the positive correlation with LSM in Model3 after adjusting all covariates ( = 0.022). The majority of dietary nutrients intake were found to have nonlinear relationships with liver fibrosis.

CONCLUSION

Overall, many nutrient variables were newly identified associations with hepatic steatosis and fibrosis. Critical threshold intake levels were revealed that may elevate disease risk. These findings may help us better understand the complex relationship between diet and hepatic steatosis and fibrosis. Moreover, this data provides critical insights for establishing evidence-based clinical nutrition strategies to optimize the prevention and management of liver diseases.

摘要

背景/目的:肝脂肪变性和肝纤维化是日益严重的全球性健康负担。迫切需要寻求早期预防和控制肝脂肪变性和肝纤维化的策略。本研究试图全面评估饮食营养摄入与肝脂肪变性和肝纤维化风险之间的关系,为医生指导患者饮食提供帮助。

方法

这项观察性研究纳入了2017年至2020年3月美国国家健康和营养检查调查队列中的15560名参与者。纳入了34项营养摄入项目。采用肝脏超声瞬时弹性成像技术评估肝脂肪变性和肝纤维化。考虑了包括社会人口学特征在内的各种变量以及其他潜在混杂因素,以确保研究结果的稳定性。此外,分析中考虑了各种协变量,并采用受限立方样条分析来检验潜在的非线性关系。分析中使用了加权分位数和(WQS)(混合效应)模型。

结果

在调整所有协变量并排除非线性相关性后,发现低碳水化合物、维生素C、吡哆醇、镁、铁和钾的摄入量与控制衰减参数(CAP)呈负相关。发现能量、维生素E、叶酸、钠、酒精、α-亚麻酸和鱼油的摄入量与肝脂肪变性之间存在非线性相关性(P<0.05)。在调整所有协变量并排除非线性相关性后(P<0.05),发现每能量单位低膳食纤维和磷的摄入量与肝脏硬度值(LSM)呈负相关。在调整所有协变量后的模型3中,高咖啡因摄入量与LSM呈正相关(P=0.022)。发现大多数饮食营养摄入量与肝纤维化存在非线性关系。

结论

总体而言,许多营养变量是新发现的与肝脂肪变性和肝纤维化相关的因素。揭示了可能增加疾病风险的关键阈值摄入量水平。这些发现可能有助于我们更好地理解饮食与肝脂肪变性和肝纤维化之间的复杂关系。此外,这些数据为制定基于证据的临床营养策略以优化肝脏疾病的预防和管理提供了关键见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e1ba/12234298/43db5a4676f7/fnut-12-1510860-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e1ba/12234298/000562d171e1/fnut-12-1510860-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e1ba/12234298/1033e7211692/fnut-12-1510860-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e1ba/12234298/c9e422f234c1/fnut-12-1510860-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e1ba/12234298/43db5a4676f7/fnut-12-1510860-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e1ba/12234298/000562d171e1/fnut-12-1510860-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e1ba/12234298/1033e7211692/fnut-12-1510860-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e1ba/12234298/c9e422f234c1/fnut-12-1510860-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e1ba/12234298/43db5a4676f7/fnut-12-1510860-g004.jpg

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