High Lipoprotein(a) May Explain One-Quarter of Clinical Familial Hypercholesterolemia Diagnoses in Danish Lipid Clinics
Bibliographic record
Abstract
CONTEXT: Cholesterol carried in lipoprotein(a) adds to measured low-density lipoprotein cholesterol (LDL-C) and may therefore drive some diagnoses of clinical familial hypercholesterolemia (FH). OBJECTIVE: We investigated plasma lipoprotein(a) in individuals referred to Danish lipid clinics and evaluated the effect of plasma lipoprotein(a) on a diagnosis of FH. METHODS: Individuals referred to 15 Danish lipid clinics who were suspected of having FH according to nationwide referral criteria were recruited between September 1, 2020 and November 30, 2021. All individuals were classified according to the Dutch Lipid Clinical Network criteria for FH before and after LDL-C was adjusted for 30% cholesterol content in lipoprotein(a). We calculated the fraction of individuals fulfilling a clinical diagnosis of FH partly due to elevated lipoprotein(a). RESULTS: We included a total of 1166 individuals for analysis, of whom 206 fulfilled a clinical diagnosis of FH. Median lipoprotein(a) was 15 mg/dL (29 nmol/L) in those referred and 28% had lipoprotein(a) greater than or equal to 50 mg/dL (105 nmol/L), while 2% had levels greater than or equal to 180 mg/dL (389 nmol/L). We found that in 27% (55/206) of those fulfilling a clinical diagnosis of FH, this was partly due to high lipoprotein(a). CONCLUSION: Elevated lipoprotein(a) was common in individuals referred to Danish lipid clinics and in one-quarter of individuals who fulfilled a clinical diagnosis of FH, this was partly due to elevated lipoprotein(a). These findings support the notion that the LPA gene should be considered an important causative gene in patients with clinical FH and further support the importance of measuring lipoprotein(a) when diagnosing FH as well as for stratification of cardiovascular 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".