Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore.
Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore; Department of Epidemiology, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR, United States.
Adv Nutr. 2024 Jul;15(7):100249. doi: 10.1016/j.advnut.2024.100249. Epub 2024 Jun 20.
With emerging Asian-derived diet quality indices and data-driven dietary patterns available, we aimed to synthesize the various dietary patterns and quantify its association with cardiovascular diseases (CVDs) among Asian populations. We systematically searched PubMed, Embase, Scopus, and Web of Science for observational studies in South, Southeast, and East Asia. Dietary patterns were grouped "high-quality," which included high intakes of three or more of the following food groups: 1) fruits and vegetables, 2) whole grains, 3) healthy protein sources (legumes and nuts, fish and seafood, low-fat dairy, and lean meat and poultry), and 4) liquid plant oils. High-quality patterns were further subcategorized based on their derivation methods: non-Asian indices, Asian indices, data-driven patterns, and plant-based indices. Dietary patterns were grouped "low-quality," which included high intakes of two or more of the following: 5) ultraprocessed food, 6) beverages and foods with added sugars, 7) foods high in salt, and 8) alcoholic beverages. Data-driven dietary patterns characterized by animal food sources were labeled "animal-based," and studies using dietary diversity scores were labeled "diet diversity indices." Dietary patterns that could not be meaningfully categorized were summarized narratively. Study-specific effect estimates were pooled using a random-effects model. Forty-one studies were included in this review. Higher adherence to high-quality dietary patterns in the top compared with bottom tertile defined by non-Asian indices (RR: 0.78; 95% CI: 0.69, 0.88; GRADE: moderate), Asian indices (RR: 0.84; 95% CI: 0.79, 0.90; GRADE: low), and data-driven patterns (RR: 0.81; 95% CI: 0.74, 0.89; GRADE: moderate) were associated with lower CVD risk. Plant-based, low-quality, animal-based, and diet diversity indices dietary patterns were not associated with CVD. Associations of Asian diet quality indices and CVD risk were weaker than those with non-Asian indices, highlighting the need for current Asian diet quality criteria to be updated to better capture the impact of diet on CVD. The systematic review and meta-analysis was registered at PROSPERO as CRD42021244318.
随着新兴的亚洲衍生的饮食质量指数和基于数据的饮食模式的出现,我们旨在综合各种饮食模式,并量化其与亚洲人群心血管疾病 (CVD) 的关系。我们系统地检索了 PubMed、Embase、Scopus 和 Web of Science 中来自南亚、东南亚和东亚的观察性研究。饮食模式被分为“高质量”,包括以下五种食物组的三种或更多种的高摄入量:1)水果和蔬菜,2)全谷物,3)健康蛋白质来源(豆类和坚果、鱼和海鲜、低脂乳制品以及瘦肉和家禽),4)液态植物油。高质量模式还根据其推导方法进一步细分为:非亚洲指数、亚洲指数、基于数据的模式和植物性指数。饮食模式被分为“低质量”,包括以下两种或更多种的高摄入量:5)超加工食品,6)含添加糖的饮料和食品,7)高盐食品,8)含酒精饮料。以动物食物来源为特征的基于数据的饮食模式被标记为“基于动物”,使用饮食多样性评分的研究被标记为“饮食多样性指数”。无法进行有意义分类的饮食模式将以叙述性方式进行总结。使用随机效应模型汇总研究特异性效应估计值。本综述共纳入 41 项研究。与非亚洲指数(RR:0.78;95%CI:0.69,0.88;GRADE:中等)、亚洲指数(RR:0.84;95%CI:0.79,0.90;GRADE:低)和数据驱动模式(RR:0.81;95%CI:0.74,0.89;GRADE:中等)定义的最高与最低三分位相比,较高的高质量饮食模式依从性与较低的 CVD 风险相关。植物性、低质量、基于动物和饮食多样性指数饮食模式与 CVD 无关。亚洲饮食质量指数与 CVD 风险的关联比与非亚洲指数的关联弱,这表明需要更新当前的亚洲饮食质量标准,以更好地捕捉饮食对 CVD 的影响。系统评价和荟萃分析已在 PROSPERO 注册为 CRD42021244318。