Life’s Essential 8 Trajectories and Risk of Stroke: A Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Evidence is lacking regarding long-term patterns of change in Life’s Essential 8 (LE8) and their association with the risk of stroke. We aim to evaluate LE8 trajectories and examine their association with the risk of stroke in China. METHODS: This study, conducted in a workplace setting, recruited 26 719 participants (average age, 46.02±11.27 years and a male population of 73.73%) who had no history of stroke and consecutively participated in 6 surveys from 2006 to 2016. Repeated LE8 measurements were determined by taking the unweighted average of the 8 component scores ranging from 0 to 100. People with higher scores had better overall cardiovascular health. By examining the medical records of the participants, stroke cases were identified for the period from 2016 to 2020. A latent mixture model was applied to classify the trajectory clusters of LE8 from 2006 to 2016, and Cox proportional hazard models were used to analyze the data. RESULTS: Five LE8 trajectories were detected between 2006 and 2016. Four hundred ninety-eight incident strokes including 55 (11.04%) hemorrhagic and 458 (91.97%) ischemic strokes were documented. After adjusting for covariates, the hazard ratios and 95% CIs for the association between stable-low, moderate-increasing, moderate-stable, and high-stable trajectories and incident stroke, compared with the moderate-decreasing trajectory, were 1.42 (1.11–1.84), 0.73 (0.56–0.96), 0.49 (0.39–0.62), and 0.19 (0.11–0.32), respectively. Individuals with high LE8 status (LE8≥80) exhibited a significantly reduced risk of stroke compared with those with low one (LE8≤49; P -trend <0.001). A faster annual growth in LE8 was related to a lower risk of stroke. CONCLUSIONS: Maintaining high LE8 over an extended period and high baseline LE8 status were related to a decreased risk of stroke. Despite the initial low level of LE8, improvement in LE8 attenuates or even reverses the risk of stroke.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".