Gender Disparities in the Association Between Educational Attainment and Cardiovascular-Kidney-Metabolic Syndrome: Cross-Sectional Study
Bibliographic record
Abstract
Background: Cardiovascular-kidney-metabolic (CKM) health is affected by social determinants of health, especially education. CKM syndrome has not been evaluated in Chinese population, and the association of education with CKM syndrome in different sexes and its intertwined relation with lifestyles have not been explored. Objective: We aimed to explore the association between educational attainment and the prevalence of CKM syndrome stages in middle-aged and older Chinese men and women as well as the potential role of health behavior based on Life's Essential 8 construct. Methods: This study used data from the nationwide, community-based REACTION (Risk Evaluation of Cancers in Chinese diabetic individuals: a longitudinal study). A total of 132,085 participants with complete information to determine CKM syndrome stage and education level were included. Educational attainment was assessed by the self-reported highest educational level achieved by the participants and recategorized as low (elementary school or no formal education) or high (middle school, high school, technical school/college, or above). CKM syndrome was ascertained and classified into 5 stages according to the American Heart Association presidential advisory released in 2023. Results: Among 132,085 participants (mean age 56.95, SD 9.19 years; n=86,675, 65.62% women) included, most had moderate-risk CKM syndrome (stages 1 and 2), and a lower proportion were at higher risk of CKM (stages 3 and 4). Along the CKM continuum, low education was associated with 34% increased odds of moderate-risk CKM syndrome for women (odds ratio 1.36, 95% CI 1.23-1.49) with a significant sex disparity, but was positively correlated with high-risk CKM for both sexes. The association between low education and high-risk CKM was more evident in women with poor health behavior but not in men, which was also interactive with and partly mediated by behavior. Conclusions: Low education was associated with adverse CKM health for both sexes but was especially detrimental to women. Such sex-specific educational disparity was closely correlated with health behavior but could not be completely attenuated by behavior modification. These findings highlight the disadvantage faced by women in CKM health ascribed to low education, underscoring the need for public health support to address this inequality.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".