Elderly patients with very high plasma lipoprotein(a) concentrations and few cardiovascular consequences: a case series
Bibliographic record
Abstract
Lipoprotein(a) (Lp(a)) is an atherogenic low-density lipoprotein (LDL)-like particle that is currently regarded as a non-modifiable risk factor for atherosclerotic cardiovascular disease. The number of patients detected with elevated Lp(a) concentrations has been increasing in recent years, although the implication of this finding is unclear for patients and physicians. We screened our lipid clinic database for patients aged >65 years with very high Lp(a) concentrations, which were defined as >230 nmol/L, and cardiovascular outcomes were assessed. The patients' (n = 16) mean (±standard deviation) age was 72.2 ± 7.1 years and the mean Lp(a) concentration was 313 ± 68 nmol/L. After a cumulative 129.0 patient-year follow-up (mean: 8.1 ± 4.2 years), the mean age was 80.3 ± 7.0 years. We observed a low baseline prevalence of cardiovascular events, with only two patients having a history of cardiovascular events. Furthermore, zero incident adverse cardiovascular events were recorded over the follow-up. Therefore, very high Lp(a) concentrations and disease-free old age are not mutually exclusive. Our aggregated clinical experience is that there is only a modest association between elevated Lp(a) concentrations and adverse outcomes. Nonetheless, we still advise treating modifiable risk factors in these patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".