Ultra-Processed Foods and Human Health: A Systematic Review and Meta-Analysis of Prospective Cohort Studies

Abstract

Evidence of associations between ultra-processed foods (UPF) and increased risk of cardiovascular disease is emerging, but it is unclear how much this is influenced by the methodology used to assess the UPF intake or by the level of consumption. We conducted a meta-analysis to evaluate 1) the association between UPF consumption and risk of diabetes, hypertension, dyslipidemia, and obesity, using prospective cohort studies; 2) the differential associations depending on the methodology used to assess UPF intake and the level of UPF consumption and 3) the quality of evidence using the NutriGrade scoring system. A systematic literature search was conducted in PubMed/MEDLINE, ISI Web of Science, and Scopus through 1 April, 2023, on studies conducted in humans providing data for the highest compared with the lowest UPF consumption categories. Summary relative ratios (RRs) and 95% confidence intervals (95% CI) were estimated using a random-effects model. Out of 4522 articles retrieved from the literature search, 25 reports met the criteria for inclusion in the meta-analysis, 7 for diabetes, 5 for hypertension, 3 for dyslipidemia, and 13 for obesity. A consistently positive association between high UPF intake and increased risk of developing diabetes (37%), hypertension (32%), hypertriglyceridemia (47%), low HDL cholesterol concentration (43%), and obesity (32%) was observed, even if the quality of evidence was not satisfying. However, these risks varied significantly depending on the methodology used to assess UPF consumption, with a difference of more than 50% between the methods. Based on the level of intake, we did not observe significant differences in the results. These findings show that UPF consumption is associated with higher risk of diabetes, hypertension, dyslipidemia, and obesity, but the level of risk consistently changes depending on the methodology used to assess UPF intake. Therefore, caution should be used when interpreting and extrapolating the results.