Extreme lipoprotein(a) levels and improved cardiovascular risk prediction
Open accessAbstract
Objectives The study tested whether extreme lipoprotein(a) levels and/or corresponding LPA risk genotypes improve myocardial infarction (MI) and coronary heart disease (CHD) risk prediction beyond conventional risk factors.Background Elevated lipoprotein(a) levels cause MI and CHD. Levels are primarily determined by variation in the LPA gene.Methods We followed 8,720 Danish participants in a general population study from 1991 to 1994 through 2011 without losses to follow-up. During this period, 730 and 1,683 first-time MI and CHD events occurred. Using predefined cutpoints for extreme lipoprotein(a) levels and/or corresponding LPA risk genotypes (kringle IV type 2 [KIV-2]) repeat polymorphism, rs3798220, and rs10455872 single nucleotide polymorphisms), we calculated net reclassification indices from <10% to 10% to 19.9% to ≥20% absolute 10-year MI and CHD risk.Results For individuals with lipoprotein(a) levels ≥80th percentile (≥47 mg/dl), 23% (p < 0.001) of MI events and 12% (p < 0.001) of CHD events were reclassified correctly, while no events were reclassified incorrectly for either endpoint. As some incorrect reclassification of individuals with no events occurred, addition of lipoprotein(a) levels ≥80th percentile overall yielded net reclassification indices of +16% (95% confidence interval: 8% to 24%) and +3% (-1% to 8%) for MI and CHD, respectively. Corresponding net reclassification indices for number of KIV-2 repeats ≤21st percentile were +12% (5% to 19%) and +4% (0% to 8%), for rs3798220 carrier status +15% (-14% to 44%) and +10% (-10% to 30%), and for rs10455872 carrier status +16% (6% to 26%) and +2% (-1% to 6%). Considering only individuals at 10% to 19.9% absolute 10-year MI and CHD risk, addition of extreme lipoprotein(a) levels or corresponding LPA risk genotypes improved risk prediction even further.Conclusions Extreme lipoprotein(a) levels or corresponding LPA KIV-2/rs10455872 risk genotypes substantially improved MI and CHD risk prediction.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Lp(a) measurement has been shown to provide clinically significant improved risk reclassification under certain conditions.
“Measurement of Lp(a) has been shown to provide clinically significant improved risk reclassification under certain conditions”
From 2019 ESC/EAS guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk (2019) · cites this paper as [110]
Including lipoprotein(a) in risk evaluation has been shown to give correct reclassification.
“Including Lp(a) in risk evaluation has been shown to give a correct reclassification 113,114 and should be considered in patients on the borderline between high and moderate risk.”
From 2016 ESC/EAS Guidelines for the Management of Dyslipidaemias (2016) · cites this paper as [114]
Including Lp(a) in risk evaluation gives correct reclassification.
“Including Lp(a) in risk evaluation has been shown to give a correct reclassification 113,114”
From 2016 ESC/EAS Guidelines for the Management of Dyslipidaemias (2016) · cites this paper as [114]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 33 · Cited by 284
Research works printed but not linked: 1
- Kamstrup et al. March 19, 2013:1146-56 Lipoprotein(a) and Cardiovascular Risk Prediction New York, NY: McGraw-Hill, Medical Publishing Division, 2001: 2753-87.
- Authors
- Kamstrup, P. R., Tybjærg-Hansen, A., & Nordestgaard, B. G.
- Affiliations
- Herlev Hospital (DK)
- University of Copenhagen (DK)
- Corresponding author
- Nordestgaard, Børge G.
- Venue
- Journal of the American College of Cardiology
- Publisher
- Elsevier BV
- Published
- 2013-03
- Volume
- 61
- Issue
- 11
- Pages
- 1146-1156
- ISSN
- 0735-1097 (ISSN-L)
- OpenAlex
- W2166005458
- Cited by
- 284
- References
- 33
- Citations by year
- 2026: 13 · 2025: 26 · 2024: 32 · 2023: 18 · 2022: 33 · 2021: 28 · 2020: 27 · 2019: 20 · 2018: 11 · 2017: 15 · 2016: 26 · 2015: 12 · 2014: 13 · 2013: 10
- FWCI
- 19.95
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of the American College of Cardiology (publishedVersion)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion)
- Funders
- Pfizer
- AstraZeneca
- Sanofi
- American College of Cardiology Foundation
- Hjerteforeningen
- Regeneron Pharmaceuticals
- Fresenius Medical Care North America
- Danmarks Frie Forskningsfond
- Topics
- Health Sciences › Medicine › Surgery › Lipoproteins and Cardiovascular Health
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Acute Myocardial Infarction Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Clinical Biochemistry › Paraoxonase enzyme and polymorphisms
- Keywords
- lipoprotein(a), LPA gene, rs3798220, rs10455872, myocardial infarction, coronary artery disease, cardiovascular risk prediction, net reclassification improvement, genetic risk variants
- MeSH terms
- Adult, Aged, Coronary Disease, Female, Genotype, Humans, Male, Middle Aged, Myocardial Infarction, Prognosis, Lipoprotein(a), Risk Assessment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review