The Relationship Between Body Mass Index (BMI) and Cardiovascular Disease Risk: A Meta-Analysis
Bibliographic record
Abstract
Cardiovascular diseases (CVDs) are the leading cause of death worldwide and Body Mass Index (BMI) is a known modifiable risk factor. The association between BMI and CVD risk is well-certified yet inconsistences exist in findings across studies, which calls for a complete meta-analysis of this association. To quantify the association between BMI and cardiovascular disease risk in populations, we performed this systematic review and meta-analysis. For instance, the study aims to estimate what is the pooled effect size for BMI (underweight, normal weight, overweight, and obese) categories and the sources that originate heterogeneity, i.e., demographic variables such as age, sex or geographical location. A literature search was conducted in PubMed, Scopus and Web of Science for observational studies published between 2000 and 2023. Only cohort, case control and cross-sectional studies assessing BMI and CVD risk in human adults were included. The meta-analysis was conducted by using the random effects model and I² statistic was used to assess heterogeneity. Age, sex and geographic region were used as subgroup analyses. Cronbach alpha and the AHRQ tool were used for assessment of risk of bias using the Newcastle-Ottawa Scale. 20 studies were included in total. The pooled OR for BMI ≥25 and CVD risk was 1.52 (95% CI: 1.30–1.77), corresponding to a 52% increased risk of CVD. The associations were stronger in older adults and women. There was significant heterogeneity and sensitivity analyses confirmed robustness of the results (I² = 65%). The risk of cardiovascular disease is strongly linked to higher BMI. Interventions for weight management targeted at patients with high exposure to CVD risk are essential to offset CVD burden, including in high-risk groups.
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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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.076 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".