The association between alcohol and metabolic syndrome: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Some studies have shown that there is a relationship between alcohol and metabolic syndrome, and some studies have not found an association; considering the importance of this issue, the aim of this study is to review all the studies related to alcohol and its relationship with metabolic syndrome and to update the information. METHODS: PubMed, Scopus and Web of Science were searched to identify studies on the association between alcohol and metabolic syndrome from January 1, 2000 through August 13, 2023. The pooled OR and the 95% confidence intervals were used to measure the association between alcohol and metabolic syndrome by assuming a random effects meta-analytic model. Quality appraisal was undertaken using the Newcastle-Ottawa Scale. RESULTS: A total of 62 studies were included. In the association between use of alcohol and metabolic syndrome, the pooled OR for cross-sectional studies was 1.12 [95% CI: 1.07, 1.18] and the pooled OR for cohort studies was 1.12 [95% CI: 1.06, 1.18]. In cohort and cross-sectional studies, the association between alcohol dose and metabolic syndrome, was not significant. For the association between frequency of alcohol consumption and metabolic syndrome, the pooled OR for under 1 to 2 times a week, 2 times to 7 times a week and above 7 drink/week were 0.85 [95% CI: 0.78, 0.93], 0.80 [95% CI: 0.63, 1.01] and 1.02 [95% CI: 0.41, 2.56], respectively. CONCLUSION: This study showed a positive relationship between alcohol consumption and metabolic syndrome. Therefore, it is recommended that further studies be conducted to accurately evaluate alcohol consumption and metabolic syndrome components, and for evaluate reducing or stopping alcohol consumption on metabolic syndrome, it is better to conduct clinical trial studies in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.038 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".