A systematic review to critically appraise methodological rigour in research on ultra-processed food and cardiovascular disease and hypertension
Mekonnen, T. C.; Bitew, Z. A.; Dessie, A. M.; Tegegne, T.; Ushula, T.; Dickinson, K.; Brady, C.; Shi, Z.; Adams, R.; Wu, J. H.; Siervo, M.; Melaku, Y. A.
Show abstract
Despite growing research linking ultra-processed food (UPF) consumption to risk of cardiovascular disease (CVD) and hypertension, no study has systematically evaluated the methodological rigor underlying these associations. We systematically searched major databases to identify eligible studies. Data were extracted for dietary assessment methods, UPF classification, covariate selection, confounding control, statistical modelling and effect estimates. Random-effects meta-analysis was conducted to pool effect estimates. Meta-regression and sensitivity analyses were performed to explore sources of heterogeneity. Substantial heterogeneity was observed in the application of the NOVA classification for categorising UPFs across the 46 eligible studies. Only two studies employed a directed acyclic graph to inform confounder selection; 43 used models with suboptimal adjustment, and 42 were overfitted due to adjustment for potential mediators. Pooled analyses indicated that higher consumption of UPFs was associated with a 9% higher risk of CVD and a 16% higher risk of hypertension, with stronger associations observed for coronary heart and cerebrovascular diseases. While higher UPF intake is consistently associated with increased risks of CVD and hypertension, methodological limitations may attenuate the observed associations. Strengthening methodological rigour through harmonised UPF classification and causal frameworks is essential to better elucidate the effect of UPF consumption on cardiometabolic health.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Relevance of Mediterranean diet as a nutritional strategy in diminishing COVID-19 risk: A systematic review 94%
- Dietary patterns of adults in Italy: Results from the third Italian National Food Consumption Survey, INRAN-SCAI 94%
- Variability in Sodium Content of Takeaway Foods: Implications for Public Health and Nutrition Policy. 94%
Similar papers in this journal
- Almond Consumption Improves Inflammatory Profiles Independent of Weight Change: A 6-Week Randomized Controlled Trial in Adults with Obesity 96%
- A Pilot Study on the Effects of Medically Supervised, Water-Only Fasting and Refeeding on Cardiometabolic Risk 94%
- Efficacy and Safety of Habitual Consumption of a Food Supplement Containing Miraculin in Malnourished Cancer Patients: the CLINMIR Pilot Study 92%
Similar papers in this journal
- Metabolomics data improve 10-year cardiovascular risk prediction with the SCORE2 algorithm for the general population without cardiovascular disease or diabetes 91%
- Age at menopause and risk of ischemic stroke: a systematic review and meta-analysis 91%
- Association of Healthy Dietary Patterns and Cardiorespiratory Fitness in the Community 90%
Similar papers in this journal
- Efficacy and safety of GLP-1 receptor agonists versus SGLT-2 inhibitors in overweight/obese patients with or without diabetes mellitus: a systematic review and network meta-analysis 92%
- A randomised controlled trial of preconception lifestyle intervention on maternal and offspring health in people with increased risk of gestational diabetes: study protocol for the BEFORE THE BEGINNING trial 92%
- Wearable Sensing in Eating Episode Monitoring: An Updated Systematic Review Protocol 92%
Similar papers in this journal
- Taxation of foods high in fat, sugar, and sodium in India: A modelling study of health and economic impacts 91%
- Predicting and elucidating the etiology of fatty liver disease using a machine learning-based approach: an IMI DIRECT study 90%
- Maternal and child gluten intake and risk of type 1 diabetes: The Norwegian Mother and Child Cohort Study 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.