Remnant cholesterol and risk of premature mortality: an analysis from a nationwide prospective cohort study
Bibliographic record
Abstract
AIMS: To explore the relationship between remnant cholesterol (RC) and the risk of premature mortality as well as life expectancy in the general population. METHODS: We included a total of 428 804 participants from the UK Biobank for analyses. Equivalent population percentiles approach based on the low-density lipoprotein cholesterol cut-off points was performed to categorize participants into three RC groups: low (with a mean RC of 0.34 mmol/L), moderate (0.53 mmol/L), and high (1.02 mmol/L). We used multivariable Cox proportional hazards models to evaluate the relationship between RC groups and the risk of premature mortality (defined as death before age 75 years). Life table methods were used to estimate life expectancy by RC groups. RESULTS: During a median follow-up of 12.1 years (Q1-Q3 11.0-13.0), there were 23 693 all-cause premature deaths documented, with an incidence of 4.83 events per 1000 person-years [95% confidence interval (CI): 4.77-4.89]. Compared with the low RC group, the moderate RC group was associated with a 9% increased risk of all-cause premature mortality [hazard ratio (HR) = 1.09, 95% CI: 1.05-1.14], while the high RC group had an 11% higher risk (HR = 1.11, 95% CI: 1.07-1.16). At the age of 50 years, high RC group was associated with an average 2.2 lower years of life expectancy for females, and an average 0.1 lower years of life expectancy for males when compared with their counterparts in the low RC group. CONCLUSIONS: Elevated RC was significantly related to an increased risk of premature mortality and a reduced life expectancy. Premature death in the general population would benefit from measurement to aid risk stratification and proactive management of RC to improve cardiovascular risk prevention efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".