Association of cardiometabolic index and risk of stroke: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Stroke remains a leading cause of morbidity and mortality worldwide, contributing significantly to public health burdens. Cardiovascular and metabolic risk factors such as diabetes, hypertension, obesity, and dyslipidaemia are strongly associated with an increased risk of stroke. The cardio-metabolic index (CMI), which integrates these factors into a single measure, has emerged as a potential predictor of stroke. This systematic review and meta-analysis intended to examine the link between CMI and risk of stroke, offering an in-depth evaluation of its predictive value METHODS: A systematic search was conducted in PubMed, Embase, and Web of Science until 10 December 2024. The inclusion criteria focused on observational studies (cohort, cross-sectional, and case-control) that reported original data on the association of CMI and stroke risk. Data extraction was standardized, and quality was assessed using the Newcastle-Ottawa Scale. Meta-analysis was performed using a random effects model in R software version 4.4 RESULTS: From 545 articles initially retrieved, with 5 studies met inclusion criteria, encompassing over 100,000 participants. Meta-analysis showed a significant association between elevated CMI and stroke risk with a pooled RR of 1.66 (95 % CI: 1.25 to 2.20). A subgroup analysis of cohort studies yielded a pooled HR of 1.63 (95 % CI: 1.21 to 2.21). There was no significant heterogeneity across studies (I² = 0 %). CONCLUSION: Our findings demonstrated a strong association between elevated CMI and an increased risk of stroke. CMI, by integrating multiple cardiovascular and metabolic factors, serves as a comprehensive predictor of stroke risk. Incorporating CMI into routine health screenings could enhance early identification and prevention efforts, ultimately aiding in the reduction of stroke incidence.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.042 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".