Association of life’s essential 8 with chronic cardiovascular-kidney disorder: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: The coexistence of cardiovascular disease and chronic kidney disease, termed chronic cardiovascular-kidney disorder (CCV-KD), is increasingly prevalent. However, limited studies have assessed the association between cardiovascular health (CVH), assessed by the American Heart Association's Life's Essential 8 (LE8), and CCV-KD. METHODS: We conducted a prospective cohort study using data from UK Biobank. Participants without cardiovascular disease and chronic kidney disease at baseline and having complete data on metrics of LE8 were included (N = 125,986). LE8 included eight metrics, and the aggregate score was categorized as low (< 50 points), intermediate (50 to < 80 points), and high (≥ 80 points), with a higher score indicating better CVH health. Adjusted Cox proportional hazard models were conducted to explore the association of CVH with the risk of CCV-KD. The adjusted proportion of population attributable risk (PAR%) was used to calculate the population-level risk caused by low or intermediate CVH. RESULTS: During a median follow-up of 12.5 years, 1,054 participants (0.8%) had incident CCV-KD. Participants with intermediate and high CVH had 54% (HR = 0.46, 95% CI: 0.40-0.54, P < 0.001) and 75% (HR = 0.25, 95% CI: 0.18-0.34, P < 0.001) lower risks of incident CCV-KD compared with those in low CVH group. There was an approximately dose-response linear relationship between the overall LE8 score and incident CCV-KD. The risk of incident CCV-KD decreased by 30% (HR = 0.70, 95% CI: 0.67-0.74, P < 0.001) for a 10-point increment of LE8 score. The adjusted PAR% of lower overall CVH was 47.4% (95% CI: 31.6%-59.8%). CONCLUSIONS: Better CVH, assessed by using LE8 score, was strongly associated with decreased risk of incident CCV-KD. These findings imply optimizing CVH may be a preventive strategy to reduce the burden of CCV-KD.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".