Association between ultra-processed foods consumption and the risk of hypertension: An umbrella review of systematic reviews
Bibliographic record
Abstract
BACKGROUND: Several systematic reviews (SRs) have investigated the association between ultra-processed foods (UPFs) and the risk of hypertension in various populations. However, the quality of the evidence remains unclear. This umbrella review was thus conducted to fill this gap. METHODS: We searched for SRs with and without meta-analyses comparing high UPF versus low UPF consumption on the risk of hypertension in the Cochrane Library, Embase, PubMed, and Web of Science from inception to August 2022. This study was registered in PROSPERO (No. CRD42022352934). The A MeaSurement Tool to Assess systematic Reviews 2 (AMSTAR-2) tool and the Preferred Reporting Item for Systematic Review and Meta-analysis 2009 (PRISMA 2009) statement were used to evaluate the methodological and reporting quality of the included SRs. Stata 15/SE was used to reanalyse the data using the random-effects model, and the risk of bias of observational studies from included SRs was reassessed using the Newcastle-Ottawa Scale (NOS) tool. The certainty of the evidence body was assessed using the GRADE recommendation. RESULTS: Seven SRs were included in the umbrella review. Among them, nine observational studies (5 cross-sectional and 4 cohort studies), whose available data were resynthesised using meta-analysis. The methodological and reporting quality of the included SRs were relatively poor. The meta-analysis results revealed suggestive evidence of an association between high UPF consumption and the incidence of hypertension (odds ratio: 1.23, 95% confidence interval: 1.11 to 1.37, p < 0.001, 95% prediction interval: 0.92 to 1.64, critically low certainty) compared to low UPF consumption. CONCLUSION: High UPF consumption is associated with an increased risk of hypertension. However, well-conducted SRs, including high-quality prospective cohort studies, are needed to further verify these findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.053 | 0.174 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.012 |
| Bibliometrics | 0.036 | 0.029 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".