Life’s Essential 8 in Relation to Cardiovascular Disease and Mortality in Individuals With Diabetes
Bibliographic record
Abstract
Background: Evidence regarding the potential health effects of Life's Essential 8 (LE8) score among individuals with type 2 diabetes (T2D) is limited. Objectives: The purpose of this study was to examine the associations of LE8 score with risk of cardiovascular disease (CVD) and mortality among individuals with T2D. Methods: We prospectively followed 19,915 Chinese participants with T2D at baseline or diagnosed during follow-up (Kailuan Study: 2006-2020), who were free of CVD at diagnosis of diabetes. Diet, lifestyle, and health conditions were repeatedly assessed every 2 years. The LE8 score (range 0-100), was calculated based on 8 components: diet quality, physical activity, smoking status, sleep health, body mass index, blood lipids, blood glucose, and blood pressure. We used time-varying cox models to model the associations. Results: During a median follow-up of 11.5 years in participants with T2D, there were 3,295 incident CVD cases and 3,123 deaths. Higher LE8 score was associated with lower risk of CVD incidence and total mortality among participants with diabetes. The multivariate-adjusted HRs for the highest quintile of LE8 score compared with the lowest quintile were 0.56 (95% CI: 0.53-0.59) for CVD, 0.57 (95% CI: 0.53-0.62) for heart disease, 0.53 (95% CI: 0.49-0.57) for stroke, and 0.73 (95% CI: 0.69-0.78) for total mortality (all P trend <0.001). Furthermore, compared with participants with stable or decreased LE8 score after diabetes diagnosis, those with increased LE8 score had 17% to 42% lower risk of CVD, heart disease, stroke, and mortality. Conclusions: A higher LE8 score was associated with a substantially lower risk of CVD incidence and total mortality among adults with T2D.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".