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  • Food Groups and Risk of Ove...
    Schlesinger, Sabrina; Neuenschwander, Manuela; Schwedhelm, Carolina; Hoffmann, Georg; Bechthold, Angela; Boeing, Heiner; Schwingshackl, Lukas

    Advances in nutrition (Bethesda, Md.), 03/2019, Volume: 10, Issue: 2
    Journal Article

    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.