Institute for Biometrics and Epidemiology, German Diabetes Center (DDZ) at Heinrich Heine University, Düsseldorf, Germany.
Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, Germany.
Adv Nutr. 2019 Mar 1;10(2):205-218. doi: 10.1093/advances/nmy092.
This meta-analysis summarizes the evidence of a prospective association between the intake of foods [whole grains, refined grains, vegetables, fruit, nuts, legumes, eggs, dairy, fish, red meat, processed meat, and sugar-sweetened beverages (SSBs)] and risk of general overweight/obesity, abdominal obesity, and weight gain. PubMed and Web of Science were searched for prospective observational studies until August 2018. Summary RRs and 95% CIs were estimated from 43 reports for the highest compared with the lowest intake categories, as well as for linear and nonlinear relations focusing on each outcome separately: overweight/obesity, abdominal obesity, and weight gain. The quality of evidence was evaluated with use of the NutriGrade tool. In the dose-response meta-analysis, inverse associations were found for whole-grain (RRoverweight/obesity: 0.93; 95% CI: 0.89, 0.96), fruit (RRoverweight/obesity: 0.93; 95% CI: 0.86, 1.00; RRweight gain: 0.91; 95% CI: 0.86, 0.97), nut (RRabdominal obesity: 0.42; 95% CI: 0.31, 0.57), legume (RRoverweight/obesity: 0.88; 95% CI: 0.84, 0.93), and fish (RRabdominal obesity: 0.83; 95% CI: 0.71, 0.97) consumption and positive associations were found for refined grains (RRoverweight/obesity: 1.05; 95% CI: 1.00, 1.10), red meat (RRabdominal obesity: 1.10; 95% CI: 1.04, 1.16; RRweight gain: 1.14; 95% CI: 1.03, 1.26), and SSBs (RRoverweight/obesity: 1.05; 95% CI: 1.00, 1.11; RRabdominal obesity: 1.12; 95% CI: 1.04, 1.20). The dose-response meta-analytical findings provided very low to low quality of evidence that certain food groups have an impact on different measurements of adiposity risk. To improve the quality of evidence, better-designed observational studies, inclusion of intervention trials, and use of novel statistical methods (e.g., substitution analyses or network meta-analyses) are needed.
这项荟萃分析总结了前瞻性研究证据,表明摄入全谷物、精制谷物、蔬菜、水果、坚果、豆类、鸡蛋、乳制品、鱼类、红肉、加工肉类和含糖饮料(SSB)与超重/肥胖、腹部肥胖和体重增加的风险之间存在关联。使用 PubMed 和 Web of Science 检索了截至 2018 年 8 月的前瞻性观察性研究。根据摄入量最高和最低的分类,对 43 项研究的汇总 RR 和 95%CI 进行了估计,此外还对每个结果进行了线性和非线性关系的聚焦分析:超重/肥胖、腹部肥胖和体重增加。使用 NutriGrade 工具评估证据质量。在剂量反应荟萃分析中,发现全谷物(超重/肥胖 RR:0.93;95%CI:0.89,0.96)、水果(超重/肥胖 RR:0.93;95%CI:0.86,1.00;体重增加 RR:0.91;95%CI:0.86,0.97)、坚果(腹部肥胖 RR:0.42;95%CI:0.31,0.57)、豆类(超重/肥胖 RR:0.88;95%CI:0.84,0.93)和鱼类(腹部肥胖 RR:0.83;95%CI:0.71,0.97)的摄入量与超重/肥胖风险呈负相关,而精制谷物(超重/肥胖 RR:1.05;95%CI:1.00,1.10)、红肉(腹部肥胖 RR:1.10;95%CI:1.04,1.16;体重增加 RR:1.14;95%CI:1.03,1.26)和 SSB(超重/肥胖 RR:1.05;95%CI:1.00,1.11;腹部肥胖 RR:1.12;95%CI:1.04,1.20)的摄入量与超重/肥胖风险呈正相关。剂量反应荟萃分析结果提供了非常低至低质量的证据,表明某些食物组对不同的肥胖风险指标有影响。为了提高证据质量,需要更好设计的观察性研究、纳入干预试验以及使用新的统计方法(例如,替代分析或网络荟萃分析)。