The association between Helicobacter pylori and obesity: a systematic review and meta-analysis of case–control studies
Bibliographic record
Abstract
INTRODUCTION: The relationship between H. pylori infection and obesity development has remained controversial among various studies. The aim of this study was to clarify the pooled effect of H. pylori infection on the development of obesity and vice versa. METHODS: We searched international databases including Medline (PubMed), Web of sciences, Scopus, EMBASE, Cochrane, Ovid, and CINHAL to retrieve all case-control studies reporting the effect of H. pylori on obesity and vice versa, which had been published in English between January 1990 and June 2019. The quality of included studies was assessed by the Modified Newcastle-Ottawa Scale for Case-Control studies. The logarithm of the odds ratio (OR) and its standard error was used for the meta-analysis. RESULTS: Eight case-control studies with 25,519 participants were included for qualitative and quantitative analyses. The pooled analysis showed that obese participants had a higher risk of H. pylori infection than lean participants with an odds ratio of 1.46 (95%CI: 1.26, 1.68). Also, the pooled analysis revealed that participants infected by H. pylori had a higher risk of obesity than non-infected participants with an odds ratio of 1.01 (95%CI: 1.01, 1.02). CONCLUSION: The results of this meta-analysis showed that there was a positive correlation between the risk of H. pylori infection and the prevalence of obesity development. Thus, H. pylori positive patients were more likely to be obese, and obese individuals had higher risks of H. pylori infection.
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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 | 0.020 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.028 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".