Cumulative exposure to atherogenic lipoprotein particles in young adults and subsequent incident atherosclerotic cardiovascular disease
Bibliographic record
Abstract
BACKGROUND AND AIMS: Atherogenic lipoprotein exposure during young adulthood increases the risk of atherosclerotic cardiovascular disease (ASCVD) later in life. The relationships between cumulative and usual yearly apolipoprotein B (apoB), low-density lipoprotein particle (LDL-P), and triglyceride-rich lipoprotein particle (TRL-P) exposure in early adult life and incident ASCVD was quantified. METHODS: Follow-up data of young adults aged 18 to <40 years from the longitudinal population-based Coronary Artery Risk Development in Young Adults (CARDIA) cohort were used. Cumulative early adult exposure of apoB, LDL-P, and TRL-P were defined over a 22-year exposure period (18 to <40 years). 'Usual' exposure to atherogenic lipid particles was calculated by dividing the cumulative exposure to apoB, LDL-P, and TRL-P by 22 years, and the hazard ratio was calculated between a 1 SD higher cumulative lipoprotein exposure with incident ASCVD after age 40 using adjusted Cox regression models. RESULTS: Among 4366 CARDIA participants, there were 241 ASCVD events after age 40 (mean follow-up of 19.3 years). A 1 SD higher cumulative exposure to apoB, LDL-P, and TRL-P was associated with unadjusted HRs of 1.53 [95% confidence interval (CI) 1.36-1.72], 1.54 (95% CI 1.36-1.75), and 1.48 (95% CI 1.30-1.68) for incident ASCVD after age 40, respectively. Adjustment for covariates yielded HRs for each measure of approximately 1.30. The hazard ratio for ASCVD increased after a usual apoB exposure of approximately 75 mg/dL/year from age 18 to <40. CONCLUSIONS: Cumulative exposure to atherogenic lipid particles in young adulthood increases the risk for incident ASCVD later in life. Apolipoprotein B concentration <75 mg/dL may represent a goal to maintain low risk in young adults.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".