Nonfasting remnant cholesterol and cardiovascular disease risk prediction in Albertans: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: European studies have shown that nonfasting remnant cholesterol can be a strong predictor of cardiovascular disease risk and may contribute to identifying residual risk; however, Canadian data are lacking on nonfasting remnant cholesterol. In this study, we aimed to determine the relation between nonfasting remnant cholesterol, low-density lipoprotein (LDL) cholesterol and cardiovascular disease among people in Alberta. METHODS: In this retrospective analysis, we used data from Alberta's Tomorrow Project, a large prospective cohort that enrolled Albertans aged 35-69 years (2000-2015). Participants with consent to data linkage, with complete nonfasting lipid data and without existing cardiovascular disease were included. The nonfasting remnant cholesterol and LDL cholesterol relation with a composite cardiovascular disease outcome of major incident cardiovascular diagnoses, ascertained by linking to Alberta Health databases, was determined by multivariable logistic regression, adjusting for age, sex, statin use, comorbidities, and LDL cholesterol or remnant cholesterol. RESULTS: The final sample of 13 988 participants was 69.4% female, and the mean age was 61.8 (standard deviation [SD] 9.7) years. Follow-up time was approximately 15 years. Mean remnant cholesterol was significantly higher among individuals with versus without cardiovascular disease (0.87 [SD 0.40] mmol/L v. 0.78 [SD 0.38] mmol/L, standardized mean difference [SMD] -0.24), and mean LDL cholesterol was significantly lower (2.69 [SD 0.93] mmol/L v. 2.88 [SD 0.84] mmol/L, SMD 0.21). The odds of incident composite cardiovascular disease were significantly increased per mmol/L increase in remnant cholesterol (adjusted odds ratio [OR] 1.48, 95% confidence interval [CI] 1.27-1.73) but significantly decreased per mmol/L increase in LDL cholesterol (adjusted OR 0.73, 95% CI 0.68-0.79). INTERPRETATION: In this large Albertan cohort of predominantly older females, nonfasting remnant cholesterol had a positive relation with cardiovascular disease incidence, whereas LDL cholesterol did not. These findings support the clinical utility of measuring non-fasting remnant cholesterol to detect cardiovascular disease risk.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".