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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".