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河北省农村成年居民膳食模式与肥胖关系:基于2022年营养与健康状况监测数据分析

Relationship between dietary patterns and obesity in rural adult residents in Hebei province: based on data from the Nutrition and Health Status Surveillance in 2022

  • 摘要:
    目的 探讨河北省农村成年居民膳食模式的特征及其与肥胖的关系,为制定针对性的农村膳食干预策略、降低肥胖率提供理论依据。
    方法 收集“2022年中国居民营养与健康状况监测”数据中河北省6个农村监测点≥18岁常住居民的人口学特征、健康状况、个人食物频率及体检数据,采用因子分析法提取膳食模式,并通过多因素logistic回归分析模型分析膳食模式与肥胖发生风险的关联。
    结果 纳入的2 479名≥18岁的农村居民中,肥胖率为25.78%,不吸烟人群肥胖率(26.74%)高于吸烟人群(22.20%),患高血压(34.16%)、糖尿病(38.60%)、血脂异常(32.74%)的人群肥胖率高于非患病人群;因子分析提取4种膳食模式,分别是动物蛋白—零食模式、蔬果模式、主食模式、面食—酒类模式,累计方差贡献率38.77%,logistic回归分析模型结果显示,调整是否吸烟、是否患高血压、糖尿病和血脂异常等混杂因素后,动物蛋白—零食为主的膳食模式高分组,发生肥胖的风险高于低分组(Q3高分组OR=1.491,95%CI=1.177~1.889,P=0.001)。
    结论 河北省农村成年居民膳食模式与肥胖存在关联,动物蛋白—零食膳食模式可能增加肥胖的患病风险。

     

    Abstract:
    Objective To investigate the association between dietary patterns and obesity among adult residents in rural Hebei province, providing a theoretical foundation for formulating dietary interventions tailored to reducing the obesity rate in this population.
    Methods The data were obtained from the Nutrition and Health Status Surveillance of Chinese Residents in 2022, including food frequency, personal information, health records, and physical examination results of permanent rural residents aged ≥ 18 years in Hebei province. Dietary patterns were identified through factor analysis. Multivariate logistic regression models were employed to examine the relationship between dietary patterns and the risk of obesity in rural adults.
    Results Among the 2 479 rural residents aged ≥ 18 years included in the study, the prevalence rate of obesity was 25.78%. The obesity rate was higher in non-smokers (26.74%) than in smokers (22.20%). Additionally, the obesity rate was higher in the populations with hypertension (34.16%), diabetes (38.60%), and dyslipidemia (32.74%) than in non-diseased populations. Four distinct dietary patterns were extracted via factor analysis as the animal protein-snack pattern, vegetable-fruit pattern, staple food pattern, and pasta-alcohol pattern, with a cumulative variance contribution rate of 38.77%. Logistic regression analysis indicated that after adjusting for confounding factors, individuals in the high-intake group of animal protein-snack pattern had higher risk of obesity than those in the low-intake group (Q3 high group OR = 1.491, 95%CI: 1.177–1.889, P = 0.001).
    Conclusions Dietary patterns are associated with obesity among rural adult residents in Hebei province, and a diet rich in animal protein-snack may elevate the risk of obesity.

     

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