Association between metabolic syndrome and myocardial infarction among patients with excess body weight: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Cardiovascular diseases (CVDs) are a major cause of morbidity and mortality worldwide. Controversial views exist over the effects of metabolically unhealthy obesity phenotypes on CVDs. This study aimed to perform a meta-analysis to assess the association between metabolic syndrome and myocardial infarction (MI) among individuals with excess body weight (EBW). METHODS: We searched PubMed/Medline, Scopus, and Web of Science databases as of December 9, 2023. Cohort studies involving patients with overweight or obesity that reported the relevant effect measures for the association between metabolic syndrome and MI were included. We excluded studies with incomplete or unavailable original data, reanalysis of previously published data, and those that did not report the adjusted effect sizes. We used the Newcastle Ottawa Scale for quality assessment. Random-effect model meta-analysis was performed. Publication bias was assessed by Begg's test. RESULTS: Overall, nine studies comprising a total of 61,104 participants were included. There was a significant positive association between metabolic syndrome and MI among those with obesity (hazard ratio (HR): 1.68; 95% confidence interval (CI): 1.27, 2.22). Subgroup analysis showed higher HRs for obesity (1.72; 1.03, 2.88) than overweight (1.58; 1.-13-2.21). Meta-regression revealed no significant association between nationality and risk of MI (p = 0.75). All studies had high qualities. There was no significant publication bias (p = 0.42). CONCLUSIONS: Metabolic syndrome increased the risk of MI in those with EBW. Further studies are recommended to investigate other risk factors of CVDs in EBW, in order to implement preventive programs to reduce the burden of CVD in obesity.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.036 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".