Food Groups and Risk of Overweight, Obesity, and Weight Gain: A Systematic Review and Dose-Response Meta-Analysis of Prospective Studies

Sabrina Schlesinger(Deutsches Diabetes-Zentrum e.V.), Manuela Neuenschwander(Deutsches Diabetes-Zentrum e.V.), Carolina Schwedhelm(German Institute of Human Nutrition), Georg Hoffmann(University of Vienna), Angela Bechthold, Heiner Boeing(German Institute of Human Nutrition), Lukas Schwingshackl(German Institute of Human Nutrition)
Advances in Nutrition
October 18, 2018
Cited by 373Open Access
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Abstract

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.


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