文章摘要
董孟茹,欧阳一非,魏艳丽,王惠君,刘爱东,王志宏,原晓蓉,董小辉,张继国.中国成年人膳食模式与血清尿酸及高尿酸血症的关联性分析[J].中华流行病学杂志,2024,45(10):1403-1409
中国成年人膳食模式与血清尿酸及高尿酸血症的关联性分析
Association of dietary patterns with serum uric acid and hyperuricemia in Chinese adults
收稿日期:2024-05-07  出版日期:2024-10-22
DOI:10.3760/cma.j.cn112338-20240507-00242
中文关键词: 膳食模式  血清尿酸  高尿酸血症
英文关键词: Dietary patterns  Serum uric acid  Hyperuricemia
基金项目:国际合作项目“中国健康与营养调查”(R01-HD30880,DK056350,R24HD050924,R01-HD38700)
作者单位E-mail
董孟茹 中国疾病预防控制中心营养与健康所, 北京 100050  
欧阳一非 中国疾病预防控制中心营养与健康所, 北京 100050  
魏艳丽 中国疾病预防控制中心营养与健康所, 北京 100050  
王惠君 中国疾病预防控制中心营养与健康所, 北京 100050
国家卫生健康委员会公共营养与健康重点实验室, 北京 100050 
 
刘爱东 中国疾病预防控制中心营养与健康所, 北京 100050
国家卫生健康委员会公共营养与健康重点实验室, 北京 100050 
 
王志宏 中国疾病预防控制中心营养与健康所, 北京 100050
国家卫生健康委员会公共营养与健康重点实验室, 北京 100050 
 
原晓蓉 中国疾病预防控制中心营养与健康所, 北京 100050  
董小辉 中国疾病预防控制中心营养与健康所, 北京 100050  
张继国 中国疾病预防控制中心营养与健康所, 北京 100050
国家卫生健康委员会公共营养与健康重点实验室, 北京 100050 
zhangjg@ninh.chinacdc.cn 
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中文摘要:
      目的 分析中国成年人的膳食模式,探索其与血清尿酸(SUA)、高尿酸血症(HUA)的关系。方法 选取2018年中国健康与营养调查中9 358名成年居民为研究对象,采用连续3天24小时膳食调查获取各类食物摄入量;通过问卷调查获得调查对象的社会人口学信息;采用因子分析法提取膳食模式,并通过多重线性回归分析膳食模式与SUA的关系,采用logistic回归模型分析HUA与膳食模式的相关性。结果 确定4种膳食模式:北方模式以小麦、其他谷类、薯类等为主要食物;现代模式以水果、奶类、蛋类、坚果等为主要食物;南方模式以蔬菜、大米等为主要食物;动物性食物-酒类模式以内脏、水产类、酒类等为主要食物。多重线性回归分析结果显示,北方模式与SUA呈负相关(β=-0.438,95%CI:-0.500~-0.376);现代模式与SUA呈负相关(β=-0.134,95%CI:-0.219~-0.049);南方模式与SUA水平较高相关(β=0.146,95%CI:0.079~0.214);动物性食物-酒类模式与SUA呈正相关(β=0.188,95%CI:0.123~0.252)。Logistic回归模型分析结果显示,与北方模式得分Q1组相比,Q3Q4组患HUA的风险均降低,OR值分别为0.777(95%CI:0.650~0.929)和0.509(95%CI:0.423~0.613);与现代模式得分Q1组相比,Q3Q4组得分越高,患HUA的风险越低,OR值分别为0.793(95%CI:0.660~0.953)和0.768(95%CI:0.631~0.934);与动物性食物-酒类模式得分Q1组相比,Q3Q4组HUA的患病风险均增加(Q3组:OR=1.224,95%CI:1.012~1.480;Q4组:OR=1.312,95%CI:1.086~1.584)。结论 膳食模式与HUA存在相关性,北方模式、现代模式与SUA和HUA的患病风险降低相关,动物性食物-酒类模式会增加HUA的患病风险。
英文摘要:
      Objective To analyze the dietary patterns of Chinese adults and explore the relationship with serum uric acid (SUA) and hyperuricemia (HUA). Methods A total of 9 358 adults were selected in the 2018 China Health and Nutrition Survey. Dietary intake data were collected by three consecutive 24-hour dietary recalls and weighing method. The social demographic information of the survey subjects was obtained through questionnaire surveys. The dietary patterns were extracted using factor analysis, and the relationship between dietary patterns and SUA was analyzed using multiple linear regression analysis. The correlation between HUA and dietary patterns was analyzed using logistic regression analysis models. Results Four dietary patterns were identified: northern (high intakes of wheat, other cereals,and tubers); modern (high intakes of fruit, dairy, eggs, and nuts); southern (high intakes of rice and vegetables);animal food-wine (high intake of organ meats, seafood, and wine). The multiple linear regression analysis results showed that the northern pattern was negatively correlated with SUA (β=-0.438, 95%CI: -0.500--0.376); the modern pattern was negatively correlated with SUA (β=-0.134, 95%CI: -0.219--0.049); the southern model was significantly correlated with higher SUA (β=0.146, 95%CI: 0.079-0.214); the animal food-wine pattern was positively correlated with SUA (β=0.188, 95%CI: 0.123-0.252). Logistic regression analysis showed that compared with the northern model score Q1 group, the risk of developing HUA was reduced in Q3and Q4 groups, with ORs values of 0.777 (95%CI: 0.650-0.929) and 0.509 (95%CI: 0.423-0.613), respectively; and compared with the modern model score Q1 group, the higher the scores in Q3 and Q4 groups, the HUA was lower, with ORs of 0.793 (95%CI: 0.660-0.953) and 0.768 (95%CI: 0.631-0.934), respectively. Compared with the animal food-wine pattern score Q1 group, the risk of developing HUA was increased in both Q3and Q4groups (Q3 group: OR=1.224, 95%CI: 1.012-1.480; Q4 group: OR=1.312, 95%CI: 1.086-1.584). Conclusions Dietary patterns are associated with HUA. The northern and modern patterns are related to lower SUA levels and reduced risk of HUA, while the animal food-wine pattern increases the risk of HUA.
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