Age and Cardiovascular Risk Attributable to Apolipoprotein B, Low‐Density Lipoprotein Cholesterol or Non‐High‐Density Lipoprotein Cholesterol
Bibliographic record
Abstract
BACKGROUND: Higher concentrations of the apolipoprotein B (apoB) lipoproteins increase the risk of cardiovascular disease. However, whether the risk associated with apoB lipoproteins varies with age has not been well examined. METHODS AND RESULTS: We determined the associations for total cholesterol, low-density lipoprotein (LDL)-cholesterol (LDL-C), non-high-density lipoprotein-cholesterol (non-HDL-C), apoB, apolipoprotein A-I (apoA-I), and HDL-cholesterol (HDL-C) with myocardial infarction at different ages in 11 760 controls and 8998 myocardial infarction cases of the INTERHEART Study. Logistic regression was used to compute the odds ratio of myocardial infarction for 1 SD change in each lipid marker by decade from <40 to >70 years of age. Except for those >70, plasma levels of total cholesterol, LDL-C, and non-HDL-C and apoB were greater in cases than controls. However, the average levels of these markers decreased significantly as age increased. By contrast, levels of apoA-I and HDL-C were significantly greater in controls than cases but increased significantly as age increased. The cardiovascular risk associated with the atherogenic lipid markers differed at different ages. Most notably, there was a significant decline in the odds ratio for total cholesterol, LDL-C, and non-HDL-C, and apoB with increases in age whereas the odds ratios associated with apoA-I and HDL-C were consistent across the age groups. CONCLUSIONS: These data indicate that the risk of cardiovascular events associated with apoB particles is greater in younger compared to older individuals. This finding is consistent with greater relative benefit from LDL-lowering therapy in younger compared to older individuals and so argues for therapy in younger individuals with elevated lipids.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".